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 "hierarchical graph network for multi hop question answering"):
  Abstract: In this paper, we present Hierarchical Graph Network (HGN) for multi-hop question answering. To aggregate clues from scattered texts across multiple paragraphs, a hierarchical graph is created by constructing nodes from different levels of granularity (i.e., questions, paragraphs, sentences, and entities), the representations of which are initialized with BERT-based context encoders. By weaving heterogeneous nodes in an integral unified graph, this characteristic hierarchical differentiation of node granularity enables HGN to support different question answering sub-tasks simultaneously (e.g., paragraph selection, supporting facts extraction, and answer prediction). Given a constructed hierarchical graph for each question, the initial node representations are updated through graph propagation; and for each sub-task, multi-hop reasoning is performed by traversing through graph edges. Extensive experiments on the HotpotQA benchmark demonstrate that the proposed HGN approach significantly outperforms prior state-of-the-art methods by a large margin in both Distractor and Fullwiki settings.

Includes members:
  Member "answering while summarizing multi task learning for multi hop qa with evidence extraction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "select answer and explain interpretable multi hop reading comprehension over multiple documents")
  Member "compositional questions do not necessitate multi hop reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a framework for evaluation of machine reading comprehension gold standards")
  Member "cognitive graph for multi hop reading comprehension at scale":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "can graph neural networks count substructures")
  Member "multi hop paragraph retrieval for open domain question answering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "differentiable reasoning over a virtual knowledge base")
  Member "learning to retrieve reasoning paths over wikipedia graph for question answering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "differentiable reasoning over a virtual knowledge base")
  Member "understanding dataset design choices for multi hop reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a framework for evaluation of machine reading comprehension gold standards")
  Member "revealing the importance of semantic retrieval for machine reading at scale":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "differentiable reasoning over a virtual knowledge base")
  Member "huggingface s transformers state of the art natural language processing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unsupervised question decomposition for question answering")

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)