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 "word subword or character an empirical study of granularity in chinese english nmt"):
  Abstract: Neural machine translation (NMT), a new approach to machine translation, has been proved to outperform conventional statistical machine translation (SMT) across a variety of language pairs. Translation is an open-vocabulary problem, but most existing NMT systems operate with a fixed vocabulary, which causes the incapability of translating rare words. This problem can be alleviated by using different translation granularities, such as character, subword and hybrid word-character. Translation involving Chinese is one of the most difficult tasks in machine translation, however, to the best of our knowledge, there has not been any other work exploring which translation granularity is most suitable for Chinese in NMT. In this paper, we conduct an extensive comparison using Chinese-English NMT as a case study. Furthermore, we discuss the advantages and disadvantages of various translation granularities in detail. Our experiments show that subword model performs best for Chinese-to-English translation with the vocabulary which is not so big while hybrid word-character model is most suitable for English-to-Chinese translation. Moreover, experiments of different granularities show that Hybrid_BPE method can achieve best result on Chinese-to-English translation task.

Includes members:
  Member "neural system combination for machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "training simplification and model simplification for deep learning a minimal effort back propagation method")
  Member "neural machine translation advised by statistical machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "cued wmt19 ewc lms")
  Member "is neural machine translation ready for deployment a case study on 30 translation directions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cued wmt19 ewc lms")
  Member "bridging neural machine translation and bilingual dictionaries":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "neural machine translation with noisy lexical constraints")
  Member "interactive attention for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "improving multi head attention with capsule networks")
  Member "achieving open vocabulary neural machine translation with hybrid word character models":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "authorship attribution in bangla literature using character level cnn")
  Member "modeling coverage for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "training simplification and model simplification for deep learning a minimal effort back propagation method")
  Member "neural machine translation with pivot languages":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ouroboros on accelerating training of transformer based language models")

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)