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 "translating pro drop languages with reconstruction models"):
  Abstract: Pronouns are frequently omitted in pro-drop languages, such as Chinese, generally leading to significant challenges with respect to the production of complete translations. To date, very little attention has been paid to the dropped pronoun (DP) problem within neural machine translation (NMT). In this work, we propose a novel reconstruction-based approach to alleviating DP translation problems for NMT models. Firstly, DPs within all source sentences are automatically annotated with parallel information extracted from the bilingual training corpus. Next, the annotated source sentence is reconstructed from hidden representations in the NMT model. With auxiliary training objectives, in terms of reconstruction scores, the parameters associated with the NMT model are guided to produce enhanced hidden representations that are encouraged as much as possible to embed annotated DP information. Experimental results on both Chinese-English and Japanese-English dialogue translation tasks show that the proposed approach significantly and consistently improves translation performance over a strong NMT baseline, which is directly built on the training data annotated with DPs.

Includes members:
  Member "translating phrases in neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi granularity self attention for neural machine translation")
  Member "learning to remember translation history with a continuous cache":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multilingual denoising pre training for neural machine translation")
  Member "chunk based bi scale decoder for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "syntax enhanced neural machine translation with syntax aware word representations")
  Member "exploiting cross sentence context for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multilingual denoising pre training for neural machine translation")
  Member "modeling source syntax for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "glyce glyph vectors for chinese character representations")
  Member "search engine guided non parametric neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "non parametric adaptation for neural machine translation")
  Member "neural machine translation advised by statistical machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "cued wmt19 ewc lms")
  Member "context gates for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "show edit and tell a framework for editing image captions")

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)