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: "relational pooling for graph representations"
  Abstract: This work generalizes graph neural networks (GNNs) beyond those based on the Weisfeiler-Lehman (WL) algorithm, graph Laplacians, and diffusions. Our approach, denoted Relational Pooling (RP), draws from the theory of finite partial exchangeability to provide a framework with maximal representation power for graphs. RP can work with existing graph representation models and, somewhat counterintuitively, can make them even more powerful than the original WL isomorphism test. Additionally, RP allows architectures like Recurrent Neural Networks and Convolutional Neural Networks to be used in a theoretically sound approach for graph classification. We demonstrate improved performance of RP-based graph representations over state-of-the-art methods on a number of tasks.

Connected through hyperedge HE-1 (induced by source paper "a survey on the expressive power of graph neural networks"):
  with members:
    - "random features strengthen graph neural networks"
    - "generalization and representational limits of graph neural networks"
    - "universal invariant and equivariant graph neural networks"
    - "exact combinatorial optimization with graph convolutional neural networks"
    - "exact k recommendation via maximal clique optimization"
    - "constant time graph neural networks"
    - "kgat knowledge graph attention network for recommendation"
    - "understanding attention and generalization in graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "deep graph matching consensus"):
  with members:
    - "simgnn a neural network approach to fast graph similarity computation"
    - "learning combinatorial embedding networks for deep graph matching"
    - "cross lingual knowledge graph alignment via graph matching neural network"
    - "computing optimal assignments in linear time for approximate graph matching"
    - "scalable gromov wasserstein learning for graph partitioning and matching"
    - "deep adversarial network alignment"
    - "graph neural networks for user identity linkage"
    - "kergm kernelized graph matching"

Connected through hyperedge HE-3 (induced by source paper "can graph neural networks count substructures"):
  with members:
    - "composing molecules with multiple property constraints"
    - "generalization and representational limits of graph neural networks"
    - "universal invariant and equivariant graph neural networks"
    - "g2sat learning to generate sat formulas"
    - "cognitive graph for multi hop reading comprehension at scale"
    - "on the universality of invariant networks"
    - "what graph neural networks cannot learn depth vs width"
    - "gnn explainer a tool for post hoc explanation of graph neural networks"

Connected through hyperedge HE-4 (induced by source paper "random features strengthen graph neural networks"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "exact k recommendation via maximal clique optimization"
    - "kgat knowledge graph attention network for recommendation"
    - "graph convolutional networks with eigenpooling"
    - "graph u nets"
    - "on the universality of invariant networks"
    - "approximation ratios of graph neural networks for combinatorial problems"
    - "what graph neural networks cannot learn depth vs width"

Connected through hyperedge HE-5 (induced by source paper "supervised learning on relational databases with graph neural networks"):
  with members:
    - "on graph classification networks datasets and baselines"
    - "deep graph library towards efficient and scalable deep learning on graphs"
    - "a comprehensive survey on graph neural networks"
    - "tabnet attentive interpretable tabular learning"
    - "neural feature learning from relational database"
    - "relational inductive biases deep learning and graph networks"
    - "how powerful are graph neural networks"
    - "decoupled weight decay regularization"

Connected through hyperedge HE-6 (induced by source paper "on the equivalence between node embeddings and structural graph representations"):
  with members:
    - "from community to role based graph embeddings"
    - "graph representation ensemble learning"
    - "is a single embedding enough learning node representations that capture multiple social contexts"
    - "on the limitations of representing functions on sets"
    - "position aware graph neural networks"
    - "what can neural networks reason about"
    - "correlated variational auto encoders"
    - "on the equivalence between graph isomorphism testing and function approximation with gnns"

Connected through hyperedge HE-7 (induced by source paper "provably powerful graph networks"):
  with members:
    - "universal invariant and equivariant graph neural networks"
    - "towards a practical k dimensional weisfeiler leman algorithm"
    - "on the universality of invariant networks"
    - "a survey on graph kernels"
    - "fast graph representation learning with pytorch geometric"
    - "dual primal graph convolutional networks"
    - "hierarchical graph representation learning with differentiable pooling"
    - "weisfeiler and leman go neural higher order graph neural networks"

Connected through hyperedge HE-8 (induced by source paper "molecule property prediction and classification with graph hypernetworks"):
  with members:
    - "hyper graph network decoders for block codes"
    - "incidence networks for geometric deep learning"
    - "on the universality of invariant networks"
    - "provably powerful graph networks"
    - "deepcode feedback codes via deep learning"
    - "graph hypernetworks for neural architecture search"
    - "weisfeiler and leman go neural higher order graph neural networks"
    - "invariant and equivariant graph networks"

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)