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 "answering while summarizing multi task learning for multi hop qa with evidence extraction"):
  Abstract: Question answering (QA) using textual sources for purposes such as reading comprehension (RC) has attracted much attention. This study focuses on the task of explainable multi-hop QA, which requires the system to return the answer with evidence sentences by reasoning and gathering disjoint pieces of the reference texts. It proposes the Query Focused Extractor (QFE) model for evidence extraction and uses multi-task learning with the QA model. QFE is inspired by extractive summarization models; compared with the existing method, which extracts each evidence sentence independently, it sequentially extracts evidence sentences by using an RNN with an attention mechanism on the question sentence. It enables QFE to consider the dependency among the evidence sentences and cover important information in the question sentence. Experimental results show that QFE with a simple RC baseline model achieves a state-of-the-art evidence extraction score on HotpotQA. Although designed for RC, it also achieves a state-of-the-art evidence extraction score on FEVER, which is a recognizing textual entailment task on a large textual database.

Includes members:
  Member "coarse grain fine grain coattention network for multi evidence question answering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi hop reading comprehension across multiple documents by reasoning over heterogeneous graphs")
  Member "team papelo transformer networks at fever":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "adversarial attacks against fact extraction and verification")
  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 "evidence sentence extraction for machine reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "reasoning driven question answering for natural language understanding")
  Member "dynamically fused graph network for multi hop reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "do multi hop question answering systems know how to answer the single hop sub questions")
  Member "combining fact extraction and verification with neural semantic matching networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "adversarial nli a new benchmark for natural language understanding")
  Member "the fact extraction and verification fever shared task":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "interpretable rumor detection in microblogs by attending to user interactions")
  Member "hotpotqa a dataset for diverse explainable multi hop question answering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a study of the tasks and models in machine reading comprehension")

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)