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 "benchmarking graph neural networks"):
  Abstract: Graph neural networks (GNNs) have become the standard toolkit for analyzing and learning from data on graphs. They have been successfully applied to a myriad of domains including chemistry, physics, social sciences, knowledge graphs, recommendation, and neuroscience. As the field grows, it becomes critical to identify the architectures and key mechanisms which generalize across graphs sizes, enabling us to tackle larger, more complex datasets and domains. Unfortunately, it has been increasingly difficult to gauge the effectiveness of new GNNs and compare models in the absence of a standardized benchmark with consistent experimental settings and large datasets. In this paper, we propose a reproducible GNN benchmarking framework, with the facility for researchers to add new datasets and models conveniently. We apply this benchmarking framework to novel medium-scale graph datasets from mathematical modeling, computer vision, chemistry and combinatorial problems to establish key operations when designing effective GNNs. Precisely, graph convolutions, anisotropic diffusion, residual connections and normalization layers are universal building blocks for developing robust and scalable GNNs.

Includes members:
  Member "understanding attention and generalization in 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 "a fair comparison of graph neural networks for graph classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bridging the gap between spatial and spectral domains a survey on graph neural networks")
  Member "are powerful graph neural nets necessary a dissection on graph classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "evaluating logical generalization in graph neural networks")
  Member "a two step graph convolutional decoder for molecule generation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "graph representation learning via graphical mutual information maximization")
  Member "on graph classification networks datasets and baselines":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an end to end graph convolutional kernel support vector machine")
  Member "self attention graph pooling":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "memory based graph networks")
  Member "gnn film graph neural networks with feature wise linear modulation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "structural language models for any code generation")
  Member "an efficient graph convolutional network technique for the travelling salesman problem":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "what graph neural networks cannot learn depth vs width")

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)