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 "learning to parse and translate improves neural machine translation"):
  Abstract: There has been relatively little attention to incorporating linguistic prior to neural machine translation. Much of the previous work was further constrained to considering linguistic prior on the source side. In this paper, we propose a hybrid model, called NMT+RNNG, that learns to parse and translate by combining the recurrent neural network grammar into the attention-based neural machine translation. Our approach encourages the neural machine translation model to incorporate linguistic prior during training, and lets it translate on its own afterward. Extensive experiments with four language pairs show the effectiveness of the proposed NMT+RNNG.

Includes members:
  Member "neural machine translation with source side latent graph parsing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "syntactically look ahead attention network for sentence compression")
  Member "towards string to tree neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "structural language models of code")
  Member "tree to sequence attentional neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks")
  Member "recurrent neural network grammars":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "hierarchical character embeddings learning phonological and semantic representations in languages of logographic origin using recursive neural networks")
  Member "globally normalized transition based neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "consistency of a recurrent language model with respect to incomplete decoding")
  Member "fully character level neural machine translation without explicit segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a brief survey of multilingual neural machine translation")
  Member "what do recurrent neural network grammars learn about syntax":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "unsupervised recurrent neural network grammars")
  Member "syntactically guided neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the linguistic representational power of neural machine translation 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)