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 "making neural machine reading comprehension faster"):
  Abstract: This study aims at solving the Machine Reading Comprehension problem where questions have to be answered given a context passage. The challenge is to develop a computationally faster model which will have improved inference time. State of the art in many natural language understanding tasks, BERT model, has been used and knowledge distillation method has been applied to train two smaller models. The developed models are compared with other models which have been developed with the same intention.

Includes members:
  Member "neural machine translation for bilingually scarce scenarios a deep multi task learning approach":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning to multi task learn for better neural machine translation")
  Member "dtmt a novel deep transition architecture for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a novel aspect guided deep transition model for aspect based sentiment analysis")
  Member "a fully attention based information retriever":
    also connected through other hyperedges:
      (none)
  Member "lightweight convolutional approaches to reading comprehension on squad":
    also connected through other hyperedges:
      (none)
  Member "phase conductor on multi layered attentions for machine comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "pullnet open domain question answering with iterative retrieval on knowledge bases and text")
  Member "qanet combining local convolution with global self attention for reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on feature normalization and data augmentation")
  Member "learning to compute word embeddings on the fly":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving pre trained multilingual models with vocabulary expansion")
  Member "contextualized word representations for reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "harry potter and the action prediction challenge from natural language")

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)