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 "select answer and explain interpretable multi hop reading comprehension over multiple documents"):
  Abstract: Interpretable multi-hop reading comprehension (RC) over multiple documents is a challenging problem because it demands reasoning over multiple information sources and explaining the answer prediction by providing supporting evidences. In this paper, we propose an effective and interpretable Select, Answer and Explain (SAE) system to solve the multi-document RC problem. Our system first filters out answer-unrelated documents and thus reduce the amount of distraction information. This is achieved by a document classifier trained with a novel pairwise learning-to-rank loss. The selected answer-related documents are then input to a model to jointly predict the answer and supporting sentences. The model is optimized with a multi-task learning objective on both token level for answer prediction and sentence level for supporting sentences prediction, together with an attention-based interaction between these two tasks. Evaluated on HotpotQA, a challenging multi-hop RC data set, the proposed SAE system achieves top competitive performance in distractor setting compared to other existing systems on the leaderboard.

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 "answering while summarizing multi task learning for multi hop qa with evidence extraction")
  Member "bag bi directional attention entity graph convolutional network for multi hop reasoning question answering":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "differentiable reasoning over a virtual knowledge base")
  Member "answering while summarizing multi task learning for multi hop qa with evidence extraction":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hierarchical graph network for multi hop question answering")
  Member "understanding dataset design choices for multi hop reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a framework for evaluation of machine reading comprehension gold standards")
  Member "evidence sentence extraction for machine reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reasoning driven question answering for natural language understanding")
  Member "multi hop reading comprehension across multiple documents by reasoning over heterogeneous graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "tensor graph convolutional networks for text classification")
  Member "roberta a robustly optimized bert pretraining approach":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "densely connected attention propagation for reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi style generative 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)