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 "does it make sense and why a pilot study for sense making and explanation"):
  Abstract: Introducing common sense to natural language understanding systems has received increasing research attention. It remains a fundamental question on how to evaluate whether a system has a sense making capability. Existing benchmarks measures commonsense knowledge indirectly and without explanation. In this paper, we release a benchmark to directly test whether a system can differentiate natural language statements that make sense from those that do not make sense. In addition, a system is asked to identify the most crucial reason why a statement does not make sense. We evaluate models trained over large-scale language modeling tasks as well as human performance, showing that there are different challenges for system sense making.

Includes members:
  Member "mcscript a novel dataset for assessing machine comprehension using script knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "commonsense reasoning for natural language understanding a survey of benchmarks resources and approaches")
  Member "think you have solved question answering try arc the ai2 reasoning challenge":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "natural language qa approaches using reasoning with external knowledge")
  Member "deep contextualized word representations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "modeling semantic plausibility by injecting world knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "can a gorilla ride a camel learning semantic plausibility from text")
  Member "bert pre training of deep bidirectional transformers for language understanding":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi simlex a large scale evaluation of multilingual and cross lingual lexical semantic similarity")
  Member "commonsenseqa a question answering challenge targeting commonsense knowledge":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "natural language qa approaches using reasoning with external knowledge")
  Member "swag a large scale adversarial dataset for grounded commonsense inference":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "natural language qa approaches using reasoning with external knowledge")
  Member "verb physics relative physical knowledge of actions and objects":
    also connected through other hyperedges:
      - Hyperedge HE-8 (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.AI(Artificial Intelligence)