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 "end to end answer chunk extraction and ranking for reading comprehension"):
  Abstract: This paper proposes dynamic chunk reader (DCR), an end-to-end neural reading comprehension (RC) model that is able to extract and rank a set of answer candidates from a given document to answer questions. DCR is able to predict answers of variable lengths, whereas previous neural RC models primarily focused on predicting single tokens or entities. DCR encodes a document and an input question with recurrent neural networks, and then applies a word-by-word attention mechanism to acquire question-aware representations for the document, followed by the generation of chunk representations and a ranking module to propose the top-ranked chunk as the answer. Experimental results show that DCR could achieve a 66.3% Exact match and 74.7% F1 score on the Stanford Question Answering Dataset.

Includes members:
  Member "attention over attention neural networks for reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi step reasoning via recurrent dual attention for visual dialog")
  Member "attentive pooling networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cut based graph learning networks to discover compositional structure of sequential video data")
  Member "a thorough examination of the cnn daily mail reading comprehension task":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "a parallel hierarchical model for machine comprehension on sparse data":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning to skim text")
  Member "attention based convolutional neural network for machine comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "reasoning driven question answering for natural language understanding")
  Member "text understanding with the attention sum reader network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (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-7 (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-8 (induced by source paper "multi step reasoning via recurrent dual attention for visual dialog")

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)