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 "can graph neural networks count substructures"):
  Abstract: The ability to detect and count certain substructures in graphs is important for solving many tasks on graph-structured data, especially in the contexts of computational chemistry and biology as well as social network analysis. Inspired by this, we propose to study the expressive power of graph neural networks (GNNs) via their ability to count attributed graph substructures, extending recent works that examine their power in graph isomorphism testing. We distinguish between two types of substructure counting: matching-count and containment-count, and establish mostly negative answers for a wide class of GNN architectures. Specifically, we prove that Message Passing Neural Networks (MPNNs), Weisfeiler-Lehman (WL) and 2-Invariant Graph Networks (2-IGNs) cannot perform matching-count of substructures consisting of 3 or more nodes, while they can perform containment-count of star-shaped substructures. We also provide partial results for k-WL and k-IGNs. We then conduct experiments that support several of the theoretical results, and demonstrate that local relational pooling strategies inspired by Murphy et al. (2019) are more effective for substructure counting. In addition, we prove that WL and 2-IGNs are equivalent in distinguishing non-isomorphic graphs, partly answering an open problem raised in Maron et al. (2019).

Includes members:
  Member "composing molecules with multiple property constraints":
    also connected through other hyperedges:
      (none)
  Member "generalization and representational limits of graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on the expressive power of graph neural networks")
  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 "g2sat learning to generate sat formulas":
    also connected through other hyperedges:
      (none)
  Member "cognitive graph for multi hop reading comprehension at scale":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "differentiable reasoning over a virtual knowledge base")
  Member "on the universality of invariant networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "what graph neural networks cannot learn depth vs width":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "gnn explainer a tool for post hoc explanation of graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bridging the gap between spatial and spectral domains a survey on graph neural networks")

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)