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 "a fully attention based information retriever"):
  Abstract: Recurrent neural networks are now the state-of-the-art in natural language processing because they can build rich contextual representations and process texts of arbitrary length. However, recent developments on attention mechanisms have equipped feedforward networks with similar capabilities, hence enabling faster computations due to the increase in the number of operations that can be parallelized. We explore this new type of architecture in the domain of question-answering and propose a novel approach that we call Fully Attention Based Information Retriever (FABIR). We show that FABIR achieves competitive results in the Stanford Question Answering Dataset (SQuAD) while having fewer parameters and being faster at both learning and inference than rival methods.

Includes members:
  Member "phase conductor on multi layered attentions for machine comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "making neural machine reading comprehension faster")
  Member "learning to compute word embeddings on the fly":
    also connected through other hyperedges:
      - Hyperedge HE-2 (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-3 (induced by source paper "making neural machine reading comprehension faster")
  Member "reinforced mnemonic reader for machine comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "conditional self attention for query based summarization")
  Member "smarnet teaching machines to read and comprehend like human":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "making neural machine reading comprehension faster")
  Member "attention is all you need":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "structural embedding of syntactic trees for machine comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a study of the tasks and models in machine reading comprehension")
  Member "stochastic answer networks for machine reading comprehension":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a study of the tasks and models in machine reading comprehension")

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)