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 character level decoder without explicit segmentation for neural machine translation"):
  Abstract: The existing machine translation systems, whether phrase-based or neural, have relied almost exclusively on word-level modelling with explicit segmentation. In this paper, we ask a fundamental question: can neural machine translation generate a character sequence without any explicit segmentation? To answer this question, we evaluate an attention-based encoder-decoder with a subword-level encoder and a character-level decoder on four language pairs--En-Cs, En-De, En-Ru and En-Fi-- using the parallel corpora from WMT'15. Our experiments show that the models with a character-level decoder outperform the ones with a subword-level decoder on all of the four language pairs. Furthermore, the ensembles of neural models with a character-level decoder outperform the state-of-the-art non-neural machine translation systems on En-Cs, En-De and En-Fi and perform comparably on En-Ru.

Includes members:
  Member "character based neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "on the linguistic representational power of neural machine translation models")
  Member "theano a python framework for fast computation of mathematical expressions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the deep learning compiler a comprehensive survey")
  Member "efficient character level document classification by combining convolution and recurrent layers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hierarchical models vs transfer learning for document level sentiment classification")
  Member "character level convolutional networks for text classification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "description based text classification with reinforcement learning")
  Member "multilingual language processing from bytes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using interlinear glosses as pivot in low resource multilingual machine translation")
  Member "character based neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using interlinear glosses as pivot in low resource multilingual machine translation")
  Member "finding function in form compositional character models for open vocabulary word representation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks")
  Member "effective approaches to attention based neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "attention over parameters for dialogue systems")

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)