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: "diffusion scattering transforms on graphs"
  Abstract: Stability is a key aspect of data analysis. In many applications, the natural notion of stability is geometric, as illustrated for example in computer vision. Scattering transforms construct deep convolutional representations which are certified stable to input deformations. This stability to deformations can be interpreted as stability with respect to changes in the metric structure of the domain. In this work, we show that scattering transforms can be generalized to non-Euclidean domains using diffusion wavelets, while preserving a notion of stability with respect to metric changes in the domain, measured with diffusion maps. The resulting representation is stable to metric perturbations of the domain while being able to capture "high-frequency" information, akin to the Euclidean Scattering.

Connected through hyperedge HE-1 (induced by source paper "scattering gcn overcoming oversmoothness in graph convolutional networks"):
  with members:
    - "lanczosnet multi scale deep graph convolutional networks"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "spectral multigraph networks for discovering and fusing relationships in molecules"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "geometric scattering for graph data analysis"
    - "neural message passing for quantum chemistry"
    - "geometric deep learning going beyond euclidean data"
    - "semi supervised classification with graph convolutional networks"

Connected through hyperedge HE-2 (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"
    - "graph convolutional neural networks via scattering"
    - "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"

Connected through hyperedge HE-3 (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"
    - "graph convolutional neural networks via scattering"
    - "a comprehensive survey on graph neural networks"
    - "autoregressive moving average graph filtering"
    - "filtering random graph processes over random time varying graphs"

Connected through hyperedge HE-4 (induced by source paper "encoding robust representation for graph generation"):
  with members:
    - "graph convolutional neural networks via scattering"
    - "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"
    - "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-5 (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 via scattering"
    - "graph convolutional neural networks for web scale recommender systems"
    - "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-6 (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"
    - "graph convolutional neural networks via scattering"
    - "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-7 (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"
    - "graph convolutional neural networks via scattering"
    - "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-8 (induced by source paper "coarse graining of data via inhomogeneous diffusion condensation"):
  with members:
    - "geometry based data generation"
    - "geometric scattering for graph data analysis"
    - "a tutorial on spectral clustering"

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.LG(Machine Learning)