The GCNN model used in the work consists of three parts, i.e., PNA convolutional layers, batch normalization layers, and fully connected layers. The model starts with a stack of six PNA layers of size 55, each of which is followed by a batch normalization layer, then goes through a global mean pooling layer, and ends with three fully connected layers with 100, 50, 25 neurons, respectively. ReLU activation functions are used.