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 "kernel graph attention network for fact verification"):
  Abstract: This paper presents Kernel Graph Attention Network (KGAT), which conducts more fine-grained evidence selection and reasoning for the fact verification task. Given a claim and a set of potential supporting evidence sentences, KGAT constructs a graph attention network using the evidence sentences as its nodes and learns to verify the claim integrity using its edge kernels and node kernels, where the edge kernels learn to propagate information across the evidence graph, and the node kernels learn to merge node level information to the graph level. KGAT reaches a comparable performance (69.4%) on FEVER, a large-scale benchmark for fact verification. Our experiments find that KGAT thrives on verification scenarios where multiple evidence pieces are required. This advantage mainly comes from the sparse and fine-grained attention mechanisms from our kernel technique.

Includes members:
  Member "several experiments on investigating pretraining and knowledge enhanced models for natural language inference":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "infusing knowledge into the textual entailment task using graph convolutional networks")
  Member "bert for evidence retrieval and claim verification":
    also connected through other hyperedges:
      (none)
  Member "reasoning over semantic level graph for fact checking":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bert for evidence retrieval and claim verification")
  Member "gear graph based evidence aggregating and reasoning for fact verification":
    also connected through other hyperedges:
      (none)
  Member "understanding attention and generalization in graph neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on the expressive power of graph neural networks")
  Member "deeper text understanding for ir with contextual neural language modeling":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "selective weak supervision for neural information retrieval")
  Member "cedr contextualized embeddings for document ranking":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "selective weak supervision for neural information retrieval")
  Member "xlnet generalized autoregressive pretraining for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on contextual embeddings")

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.CL(Computation and Language)