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 "natural language qa approaches using reasoning with external knowledge"):
  Abstract: Question answering (QA) in natural language (NL) has been an important aspect of AI from its early days. Winograd's ``councilmen'' example in his 1972 paper and McCarthy's Mr. Hug example of 1976 highlights the role of external knowledge in NL understanding. While Machine Learning has been the go-to approach in NL processing as well as NL question answering (NLQA) for the last 30 years, recently there has been an increasingly emphasized thread on NLQA where external knowledge plays an important role. The challenges inspired by Winograd's councilmen example, and recent developments such as the Rebooting AI book, various NLQA datasets, research on knowledge acquisition in the NLQA context, and their use in various NLQA models have brought the issue of NLQA using ``reasoning'' with external knowledge to the forefront. In this paper, we present a survey of the recent work on them. We believe our survey will help establish a bridge between multiple fields of AI, especially between (a) the traditional fields of knowledge representation and reasoning and (b) the field of NL understanding and NLQA.

Includes members:
  Member "transformers as soft reasoners over language":
    also connected through other hyperedges:
      (none)
  Member "recent advances in natural language inference a survey of benchmarks resources and approaches":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "towards a framework for visual intelligence in service robotics epistemic requirements and gap analysis")
  Member "boolq exploring the surprising difficulty of natural yes no questions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "tydi qa a benchmark for information seeking question answering in typologically diverse languages")
  Member "piqa reasoning about physical commonsense in natural language":
    also connected through other hyperedges:
      (none)
  Member "language models as knowledge bases":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sbert wk a sentence embedding method by dissecting bert based word models")
  Member "quoref a reading comprehension dataset with questions requiring coreferential reasoning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "beat the ai investigating adversarial human annotations for reading comprehension")
  Member "understanding roles and entities datasets and models for natural language inference":
    also connected through other hyperedges:
      (none)
  Member "qasc a dataset for question answering via sentence composition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generating followup questions for interpretable multi hop 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)