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 "newsqa a machine comprehension dataset"):
  Abstract: We present NewsQA, a challenging machine comprehension dataset of over 100,000 human-generated question-answer pairs. Crowdworkers supply questions and answers based on a set of over 10,000 news articles from CNN, with answers consisting of spans of text from the corresponding articles. We collect this dataset through a four-stage process designed to solicit exploratory questions that require reasoning. A thorough analysis confirms that NewsQA demands abilities beyond simple word matching and recognizing textual entailment. We measure human performance on the dataset and compare it to several strong neural models. The performance gap between humans and machines (0.198 in F1) indicates that significant progress can be made on NewsQA through future research. The dataset is freely available at this https URL.

Includes members:
  Member "a thorough examination of the cnn daily mail reading comprehension task":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "non autoregressive neural dialogue generation")
  Member "text understanding with the attention sum reader network":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "retrospective reader for machine reading comprehension")
  Member "squad 100 000 questions for machine comprehension of text":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "iterative alternating neural attention for machine reading":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi step reasoning via recurrent dual attention for visual dialog")
  Member "embracing data abundance booktest dataset for reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "investigating prior knowledge for challenging chinese machine reading comprehension")
  Member "multi perspective context matching for machine comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "description based text classification with reinforcement learning")
  Member "machine comprehension using match lstm and answer pointer":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "description based text classification with reinforcement learning")

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)