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: "spectral multigraph networks for discovering and fusing relationships in molecules"
  Abstract: Spectral Graph Convolutional Networks (GCNs) are a generalization of convolutional networks to learning on graph-structured data. Applications of spectral GCNs have been successful, but limited to a few problems where the graph is fixed, such as shape correspondence and node classification. In this work, we address this limitation by revisiting a particular family of spectral graph networks, Chebyshev GCNs, showing its efficacy in solving graph classification tasks with a variable graph structure and size. Chebyshev GCNs restrict graphs to have at most one edge between any pair of nodes. To this end, we propose a novel multigraph network that learns from multi-relational graphs. We model learned edges with abstract meaning and experiment with different ways to fuse the representations extracted from annotated and learned edges, achieving competitive results on a variety of chemical classification benchmarks.

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"
    - "deeper insights into graph convolutional networks for semi supervised learning"
    - "diffusion scattering transforms on graphs"
    - "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 "image classification with hierarchical multigraph networks"):
  with members:
    - "graph u nets"
    - "iterative visual reasoning beyond convolutions"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "hierarchical graph representation learning with differentiable pooling"
    - "relational inductive biases deep learning and graph networks"
    - "how powerful are graph neural networks"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"

Connected through hyperedge HE-3 (induced by source paper "first and complementary neighborhood combination of adjacency matrix for graph learning"):
  with members:
    - "gmnn graph markov neural networks"
    - "graph neural networks with convolutional arma filters"
    - "mixhop higher order graph convolutional architectures via sparsified neighborhood mixing"
    - "learning discrete structures for graph neural networks"
    - "representation learning on graphs with jumping knowledge networks"
    - "how powerful are graph neural networks"
    - "mine mutual information neural estimation"
    - "dynamic edge conditioned filters in convolutional neural networks on graphs"

Connected through hyperedge HE-4 (induced by source paper "variations on the chebyshev lagrange activation function"):
  with members:
    - "detecting linguistic characteristics of alzheimer s dementia by interpreting neural models"
    - "polynomial regression as an alternative to neural nets"
    - "semi supervised classification by reaching consensus among modalities"
    - "the effect of heterogeneous data for alzheimer s disease detection from speech"
    - "deepconsensus using the consensus of features from multiple layers to attain robust image classification"
    - "on the importance of normative data in speech based assessment"
    - "shake shake regularization"
    - "convolutional neural networks on graphs with fast localized spectral filtering"

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)