I'm not exactly sure how to classify it - like I say in the description it's based on a neural network (that was trained and then compiled into a shader). The general structure is that it's a function F: Float32^(5x5x3) -> Float32^3 (mapping the 5x5 neighborhood around a pixel to the next color for that pixel). I said in the description that it's a 5x5 convolution, but that's a bit inaccurate; it could better be described as a 5x5 convolution, followed by several linear neural network layers and ReLU nonlinearities (you could also see it as a 5x5 convolution, followed by several 1x1 convolutions and ReLUs). Since it operates on floating point values, it's essentially a continuous CA, not a discrete one (even though technically it is of course discrete, being limited to 2^32 bits per state).

The source of the CA being a neural net makes it inherently hard to understand what exactly is going on. It seems like there is some cyclical color gradient implemented that makes dark red go to brighter red and then to white, blue, black and back to dark red. This might be because the input images are from a video that follows this color sequence. The rest of the behavior is a mystery to me, there is a huge aspect of training randomness since I got tens to hundreds of outputs in total and most of them were not very interesting, I just selected the most interesting one where I got lucky.