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: "discriminative structural graph classification"
  Abstract: This paper focuses on the discrimination capacity of aggregation functions: these are the permutation invariant functions used by graph neural networks to combine the features of nodes. Realizing that the most powerful aggregation functions suffer from a dimensionality curse, we consider a restricted setting. In particular, we show that the standard sum and a novel histogram-based function have the capacity to discriminate between any fixed number of inputs chosen by an adversary. Based on our insights, we design a graph neural network aiming, not to maximize discrimination capacity, but to learn discriminative graph representations that generalize well. Our empirical evaluation provides evidence that our choices can yield benefits to the problem of structural graph classification.

Connected through hyperedge HE-1 (induced by source paper "what graph neural networks cannot learn depth vs width"):
  with members:
    - "edgenets edge varying graph neural networks"
    - "universal invariant and equivariant graph neural networks"
    - "lanczosnet multi scale deep graph convolutional networks"
    - "mincut pooling in graph neural networks"
    - "break the ceiling stronger multi scale deep graph convolutional networks"
    - "understanding the representation power of graph neural networks in learning graph topology"
    - "on the universality of invariant networks"
    - "approximation ratios of graph neural networks for combinatorial problems"

Connected through hyperedge HE-2 (induced by source paper "unsupervised universal self attention network for graph classification"):
  with members:
    - "a survey on graph kernels"
    - "are powerful graph neural nets necessary a dissection on graph classification"
    - "provably powerful graph networks"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "hierarchical graph representation learning with differentiable pooling"
    - "deep learning on graphs a survey"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-3 (induced by source paper "universal self attention network for graph classification"):
  with members:
    - "are powerful graph neural nets necessary a dissection on graph classification"
    - "graph neural networks a review of methods and applications"
    - "relational inductive biases deep learning and graph networks"
    - "anonymous walk embeddings"
    - "network representation learning a survey"
    - "attention is all you need"
    - "dynamic routing between capsules"
    - "neural message passing for quantum chemistry"

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)