human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "generalization and representational limits of graph neural networks"):
  Abstract: We address two fundamental questions about graph neural networks (GNNs). First, we prove that several important graph properties cannot be computed by GNNs that rely entirely on local information. Such GNNs include the standard message passing models, and more powerful spatial variants that exploit local graph structure (e.g., via relative orientation of messages, or local port ordering) to distinguish neighbors of each node. Our treatment includes a novel graph-theoretic formalism. Second, we provide the first data dependent generalization bounds for message passing GNNs. This analysis explicitly accounts for the local permutation invariance of GNNs. Our bounds are much tighter than existing VC-dimension based guarantees for GNNs, and are comparable to Rademacher bounds for recurrent neural networks.

Includes members:
  Member "directional message passing for molecular graphs":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "neural message passing on high order paths")
  Member "efficient probabilistic logic reasoning with graph neural networks":
    also connected through other hyperedges:
      (none)
  Member "universal invariant and equivariant graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "graph transformer networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "heterogeneous graph transformer")
  Member "understanding the representation power of graph neural networks in learning graph topology":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "modeling global and local node contexts for text generation from knowledge graphs")
  Member "on the universality of invariant networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "approximation ratios of graph neural networks for combinatorial problems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "a survey on graph kernels":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "few shot learning on graphs via super classes based on graph spectral measures")

Question: Please tell me which class the source paper 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)