$\newcommand{\Si}{\Sigma}\newcommand{\R}{\mathbb R}$First, one should not denote a random vector in $\R^n$ (which is not actually a vector in $\R^n$ but a function with values in $\R^n$) and a true, non-random vector in $\R^n$ by the same symbol, such as $\mathbf x$. 

Accordingly, let $X\sim N(0,S)$, where $S:=H^{-1}$. Let $c:=C$ and $g:=\mathbf g\ne0$. We want to find the conditional density of $X$ given $X^\top g=c$; as usual, we identify $\R^n$ with the set $\R^{n\times1}$ of all $n\times1$ real matrices. Without loss of generality, 
$g$ is a unit vector; otherwise, replace $g$ and $c$ by $g/|g|$ and $c/|g|$, respectively, where $|g|$ is the Euclidean norm of $g$. Let 
$g_1,\dots,g_n$
be any orthonormal vectors in $\R^n$ such as $g_n=g$. 

Let $Y_1,\dots,Y_n$ be the coordinates of the random vector $X$ in the orthonormal basis $(g_1,\dots,g_n)$, so that $Y_k=g_k^\top X$ and $X=\sum_{k=1}^n Y_kg_k$. Let $Y:=[Y_1,\dots,Y_n]^\top$, so that 
$Y=G^\top X$ and $X=GY$, where $G:=[g_1,\dots,g_n]$, the $n\times n$ matrix with columns $g_1,\dots,g_n$. The covariance matrix of $Y$ is 
\begin{equation}
	EYY^\top=G^\top SG=:
	\begin{bmatrix}
	\Si_{11}&\Si_{12} \\ 
	\Si_{21}&\Si_{22} 
	\end{bmatrix},
\end{equation}
where $\Si_{12}$ is the $(n-1)\times1$ matrix that is the covariance matrix of $[Y_1,\dots,Y_{n-1}]^\top$ and $Y_n$. Also, $Y\sim N(0,G^\top SG)$. 
So, the [conditional pdf][1] of $[Y_1,\dots,Y_{n-1}]^\top$ given $Y_n=c$ is the pdf, say $p_c$, of the $(n-1)$-dimensional normal distribution 
\begin{equation}
	N(\Si_{12}\Si_{22}^{-1}c,\Si_{11}-\Si_{12}\Si_{22}^{-1}\Si_{21}).
\end{equation} 

This conditional pdf, $p_c$, can be regarded as the desired conditional density of $X$ given $X^\top g=c$. Indeed, $p_c$ can be used as follows: for any (say) nonnegative Borel function $f\colon\R^n\to\R$, 
\begin{equation}
	E(f(X)|X^\top g=c)=E(f(GY)|Y_n=c) \\ 
	=\int_{\R^{n-1}}f(G[y_1,\dots,y_{n-1},c]^\top)p_c(y_1,\dots,y_{n-1})\,
	dy_1\cdots dy_{n-1}. 
\end{equation} 
In particular, for any Borel subset $B$ of $\R^n$, 
\begin{equation}
	P(X\in B|X^\top g=c)=P(GY\in B|Y_n=c) \\ 
	=\int_{\R^{n-1}}1(G[y_1,\dots,y_{n-1},c]^\top\in B)p_c(y_1,\dots,y_{n-1})\,
	dy_1\cdots dy_{n-1}. 
\end{equation} 

[1]: https://en.wikipedia.org/wiki/Multivariate_normal_distribution#Conditional_distributions