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: "clique pooling for graph classification"
  Abstract: We propose a novel graph pooling operation using cliques as the unit pool. As this approach is purely topological, rather than featural, it is more readily interpretable, a better analogue to image coarsening than filtering or pruning techniques, and entirely nonparametric. The operation is implemented within graph convolution network (GCN) and GraphSAGE architectures and tested against standard graph classification benchmarks. In addition, we explore the backwards compatibility of the pooling to regular graphs, demonstrating competitive performance when replacing two-by-two pooling in standard convolutional neural networks (CNNs) with our mechanism.

Connected through hyperedge HE-1 (induced by source paper "memory based graph networks"):
  with members:
    - "mincut pooling in graph neural networks"
    - "graph u nets"
    - "generating diverse high fidelity images with vq vae 2"
    - "relational graph representation learning for open domain question answering"
    - "self attention graph pooling"
    - "unsupervised multi task feature learning on point clouds"
    - "large memory layers with product keys"
    - "a comprehensive survey on graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "deep graph mapper seeing graphs through the neural lens"):
  with members:
    - "mincut pooling in graph neural networks"
    - "asap adaptive structure aware pooling for learning hierarchical graph representations"
    - "graph convolutional networks with eigenpooling"
    - "graph u nets"
    - "on graph classification networks datasets and baselines"
    - "self attention graph pooling"
    - "graph filtration learning"
    - "gnnexplainer generating explanations for graph neural networks"

Connected through hyperedge HE-3 (induced by source paper "hierarchical representation learning in graph neural networks with node decimation pooling"):
  with members:
    - "mincut pooling in graph neural networks"
    - "understanding attention and generalization in graph neural networks"
    - "graph neural networks with convolutional arma filters"
    - "towards interpretable sparse graph representation learning with laplacian pooling"
    - "graph u nets"
    - "edge contraction pooling for graph neural networks"
    - "a non negative factorization approach to node pooling in graph convolutional neural networks"
    - "hierarchical graph representation learning with differentiable pooling"

Connected through hyperedge HE-4 (induced by source paper "mincut pooling in graph neural networks"):
  with members:
    - "gap generalizable approximate graph partitioning framework"
    - "graph neural networks with convolutional arma filters"
    - "graph u nets"
    - "a comprehensive survey on graph neural networks"
    - "hierarchical graph representation learning with differentiable pooling"
    - "relational inductive biases deep learning and graph networks"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"
    - "dynamic edge conditioned filters in convolutional neural 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.LG(Machine Learning)