human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "graph convolutional neural networks via scattering"
  Abstract: Abstract   We generalize the scattering transform to graphs and consequently construct a convolutional neural network on graphs. We show that under certain conditions, any feature generated by such a network is approximately invariant to permutations and stable to signal and graph manipulations. Numerical results demonstrate competitive performance on relevant datasets.

Connected through hyperedge HE-1 (induced by source paper "human action recognition with multi laplacian graph convolutional networks"):
  with members:
    - "graph neural networks with convolutional arma filters"
    - "monte carlo convolution for learning on non uniformly sampled point clouds"
    - "simplifying graph convolutional networks"
    - "graph wavelet neural network"
    - "a comprehensive survey on graph neural networks"
    - "part based graph convolutional network for action recognition"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "predict then propagate graph neural networks meet personalized pagerank"

Connected through hyperedge HE-2 (induced by source paper "transferability of spectral graph convolutional neural networks"):
  with members:
    - "graph neural networks with convolutional arma filters"
    - "stability of graph scattering transforms"
    - "stability properties of graph neural networks"
    - "on the transferability of spectral graph filters"
    - "a comprehensive survey on graph neural networks"
    - "diffusion scattering transforms on graphs"
    - "autoregressive moving average graph filtering"
    - "filtering random graph processes over random time varying graphs"

Connected through hyperedge HE-3 (induced by source paper "encoding robust representation for graph generation"):
  with members:
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "generative networks as inverse problems with scattering transforms"
    - "diffusion scattering transforms on graphs"
    - "junction tree variational autoencoder for molecular graph generation"
    - "designing random graph models using variational autoencoders with applications to chemical design"
    - "molecular de novo design through deep reinforcement learning"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-4 (induced by source paper "stability properties of graph neural networks"):
  with members:
    - "learning decentralized controllers for robot swarms with graph neural networks"
    - "on the transferability of spectral graph filters"
    - "graph convolutional neural networks for web scale recommender systems"
    - "diffusion scattering transforms on graphs"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"

Connected through hyperedge HE-5 (induced by source paper "stability of graph neural networks to relative perturbations"):
  with members:
    - "controllability of bandlimited graph processes over random time varying graphs"
    - "learning decentralized controllers for robot swarms with graph neural networks"
    - "gated graph convolutional recurrent neural networks"
    - "stability properties of graph neural networks"
    - "diffusion scattering transforms on graphs"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "network topology inference from spectral templates"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-6 (induced by source paper "stability of graph scattering transforms"):
  with members:
    - "learning decentralized controllers for robot swarms with graph neural networks"
    - "on the transferability of spectral graph filters"
    - "diffusion scattering transforms on graphs"
    - "geometric deep learning going beyond euclidean data"
    - "network topology inference from spectral templates"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "authorship attribution through function word adjacency networks"
    - "discrete signal processing on graphs frequency analysis"

Connected through hyperedge HE-7 (induced by source paper "geometric scattering for graph data analysis"):
  with members:
    - "classification with joint time frequency scattering"
    - "generative networks as inverse problems with scattering transforms"
    - "diffusion scattering transforms on graphs"
    - "how powerful are graph neural networks"
    - "covariant compositional networks for learning graphs"
    - "geometric deep learning going beyond euclidean data"
    - "graph classification with 2d convolutional neural networks"
    - "graph2vec learning distributed representations of graphs"

Connected through hyperedge HE-8 (induced by source paper "diffusion scattering transforms on graphs"):
  with members:
    - "neural message passing for quantum chemistry"
    - "geometric deep learning going beyond euclidean data"
    - "cayleynets graph convolutional neural networks with complex rational spectral filters"
    - "deep convolutional networks on graph structured data"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"
    - "authorship attribution through function word adjacency networks"
    - "deep scattering spectrum"
    - "spectral networks and locally connected networks on graphs"

Question: Please tell me which class the center node belongs to. The 40 classes are: cs.NA(Numerical Analysis), cs.MM(Multimedia), cs.LO(Logic in Computer Science), cs.CY(Computers and Society), cs.CR(Cryptography and Security), cs.DC(Distributed, Parallel, and Cluster Computing), cs.HC(Human-Computer Interaction), cs.CE(Computational Engineering, Finance, and Science), cs.NI(Networking and Internet Architecture), cs.CC(Computational Complexity), cs.AI(Artificial Intelligence), cs.MA(Multiagent Systems), cs.GL(General Literature), cs.NE(Neural and Evolutionary Computing), cs.SC(Symbolic Computation), cs.AR(Hardware Architecture), cs.CV(Computer Vision and Pattern Recognition), cs.GR(Graphics), cs.ET(Emerging Technologies), cs.SY(Systems and Control), cs.CG(Computational Geometry), cs.OH(Other Computer Science), cs.PL(Programming Languages), cs.SE(Software Engineering), cs.LG(Machine Learning), cs.SD(Sound), cs.SI(Social and Information Networks), cs.RO(Robotics), cs.IT(Information Theory), cs.PF(Performance), cs.CL(Computation and Language), cs.IR(Information Retrieval), cs.MS(Mathematical Software), cs.FL(Formal Languages and Automata Theory), cs.DS(Data Structures and Algorithms), cs.OS(Operating Systems), cs.GT(Computer Science and Game Theory), cs.DB(Databases), cs.DL(Digital Libraries), cs.DM(Discrete Mathematics). Directly output the class name.

gpt: cs.IT(Information Theory)