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 "ms marco a human generated machine reading comprehension dataset"):
  Abstract: We introduce a large scale MAchine Reading COmprehension dataset, which we name MS MARCO. The dataset comprises of 1,010,916 anonymized questions---sampled from Bing's search query logs---each with a human generated answer and 182,669 completely human rewritten generated answers. In addition, the dataset contains 8,841,823 passages---extracted from 3,563,535 web documents retrieved by Bing---that provide the information necessary for curating the natural language answers. A question in the MS MARCO dataset may have multiple answers or no answers at all. Using this dataset, we propose three different tasks with varying levels of difficulty: (i) predict if a question is answerable given a set of context passages, and extract and synthesize the answer as a human would (ii) generate a well-formed answer (if possible) based on the context passages that can be understood with the question and passage context, and finally (iii) rank a set of retrieved passages given a question. The size of the dataset and the fact that the questions are derived from real user search queries distinguishes MS MARCO from other well-known publicly available datasets for machine reading comprehension and question-answering. We believe that the scale and the real-world nature of this dataset makes it attractive for benchmarking machine reading comprehension and question-answering models.

Includes members:
  Member "race large scale reading comprehension dataset from examinations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reclor a reading comprehension dataset requiring logical reasoning")
  Member "searchqa a new q a dataset augmented with context from a search engine":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "the narrativeqa reading comprehension challenge":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "reasonet learning to stop reading in machine comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "description based text classification with reinforcement learning")
  Member "dureader a chinese machine reading comprehension dataset from real world applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "simple and effective multi paragraph reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "newsqa a machine comprehension dataset":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "long short term memory networks for machine reading":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "time aware large kernel convolutions")

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)