Not really. There are many things named after Gauss but a "Gaussian" is almost always meant to be a probability distribution / density function that is very well understood and defined (and common)
Seems like they just mean the vector version of a Gaussian function: f(r) = exp(-r•r). Basically a "bell curve" except in 3D so it's a ball that's dense at the center and dies off. Then the optimizer might learn to produce an intensity, offset, and width for each point in a cloud, so the A,B,C for A*f((r-B)/C) at each point or something.
This is in fact what the optimizer does. At least in the original paper, the model learns to skew and rotate the gaussians.
Take a linear algebra course or read a textbook before trying to read and understand cutting edge ML research!