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 "embracing data abundance booktest dataset for reading comprehension"):
  Abstract: There is a practically unlimited amount of natural language data available. Still, recent work in text comprehension has focused on datasets which are small relative to current computing possibilities. This article is making a case for the community to move to larger data and as a step in that direction it is proposing the BookTest, a new dataset similar to the popular Children's Book Test (CBT), however more than 60 times larger. We show that training on the new data improves the accuracy of our Attention-Sum Reader model on the original CBT test data by a much larger margin than many recent attempts to improve the model architecture. On one version of the dataset our ensemble even exceeds the human baseline provided by Facebook. We then show in our own human study that there is still space for further improvement.

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 "dataset and neural recurrent sequence labeling model for open domain factoid question answering":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "investigating prior knowledge for challenging chinese machine reading comprehension")
  Member "wikireading a novel large scale language understanding task over wikipedia":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "kleister a novel task for information extraction involving long documents with complex layout")
  Member "squad 100 000 questions for machine comprehension of text":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bert can see out of the box on the cross modal transferability of text representations")
  Member "the lambada dataset word prediction requiring a broad discourse context":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on contextual embeddings")
  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)