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 "qasc a dataset for question answering via sentence composition"):
  Abstract: Composing knowledge from multiple pieces of texts is a key challenge in multi-hop question answering. We present a multi-hop reasoning dataset, Question Answering via Sentence Composition(QASC), that requires retrieving facts from a large corpus and composing them to answer a multiple-choice question. QASC is the first dataset to offer two desirable properties: (a) the facts to be composed are annotated in a large corpus, and (b) the decomposition into these facts is not evident from the question itself. The latter makes retrieval challenging as the system must introduce new concepts or relations in order to discover potential decompositions. Further, the reasoning model must then learn to identify valid compositions of these retrieved facts using common-sense reasoning. To help address these challenges, we provide annotation for supporting facts as well as their composition. Guided by these annotations, we present a two-step approach to mitigate the retrieval challenges. We use other multiple-choice datasets as additional training data to strengthen the reasoning model. Our proposed approach improves over current state-of-the-art language models by 11% (absolute). The reasoning and retrieval problems, however, remain unsolved as this model still lags by 20% behind human performance.

Includes members:
  Member "on the capabilities and limitations of reasoning for natural language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reasoning driven question answering for natural language understanding")
  Member "improving question answering with external knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sirenless reveal the intention behind news")
  Member "question answering as global reasoning over semantic abstractions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bridging knowledge graphs to generate scene graphs")
  Member "drop a reading comprehension benchmark requiring discrete reasoning over paragraphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "natural language qa approaches using reasoning with external knowledge")
  Member "what s missing a knowledge gap guided approach for multi hop question answering":
    also connected through other hyperedges:
      (none)
  Member "a systematic classification of knowledge reasoning and context within the arc dataset":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a framework for evaluation of machine reading comprehension gold standards")
  Member "from recognition to cognition visual commonsense reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "weakly supervised visual semantic parsing")
  Member "can a suit of armor conduct electricity a new dataset for open book question answering":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "natural language qa approaches using reasoning with external knowledge")

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)