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 "modeling source syntax for neural machine translation"):
  Abstract: Even though a linguistics-free sequence to sequence model in neural machine translation (NMT) has certain capability of implicitly learning syntactic information of source sentences, this paper shows that source syntax can be explicitly incorporated into NMT effectively to provide further improvements. Specifically, we linearize parse trees of source sentences to obtain structural label sequences. On the basis, we propose three different sorts of encoders to incorporate source syntax into NMT: 1) Parallel RNN encoder that learns word and label annotation vectors parallelly; 2) Hierarchical RNN encoder that learns word and label annotation vectors in a two-level hierarchy; and 3) Mixed RNN encoder that stitchingly learns word and label annotation vectors over sequences where words and labels are mixed. Experimentation on Chinese-to-English translation demonstrates that all the three proposed syntactic encoders are able to improve translation accuracy. It is interesting to note that the simplest RNN encoder, i.e., Mixed RNN encoder yields the best performance with an significant improvement of 1.4 BLEU points. Moreover, an in-depth analysis from several perspectives is provided to reveal how source syntax benefits NMT.

Includes members:
  Member "neural machine translation advised by statistical machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cued wmt19 ewc lms")
  Member "tree to sequence attentional neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks")
  Member "context gates for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "show edit and tell a framework for editing image captions")
  Member "neural machine translation with reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bi decoder augmented network for neural machine translation")
  Member "modeling coverage for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "training simplification and model simplification for deep learning a minimal effort back propagation method")
  Member "factored 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 "supervised attentions for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "relevance promoting language model for short text conversation")
  Member "linguistic input features improve neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "augmenting neural machine translation with knowledge graphs")

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)