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 "improving neural machine translation with parent scaled self attention"):
  Abstract: Most neural machine translation (NMT) models operate on source and target sentences, treating them as sequences of words and neglecting their syntactic structure. Recent studies have shown that embedding the syntax information of a source sentence in recurrent neural networks can improve their translation accuracy, especially for low-resource language pairs. However, state-of-the-art NMT models are based on self-attention networks (e.g., Transformer), in which it is still not clear how to best embed syntactic information. In this work, we explore different approaches to make such models syntactically aware. Moreover, we propose a novel method to incorporate syntactic information in the self-attention mechanism of the Transformer encoder by introducing attention heads that can attend to the dependency parent of each token. The proposed model is simple yet effective, requiring no additional parameter and improving the translation quality of the Transformer model especially for long sentences and low-resource scenarios. We show the efficacy of the proposed approach on NC11 English-German, WMT16 and WMT17 English-German, WMT18 English-Turkish, and WAT English-Japanese translation tasks.

Includes members:
  Member "context aware self attention networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "normalized and geometry aware self attention network for image captioning")
  Member "modeling latent sentence structure in neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "findings of the second workshop on neural machine translation and generation")
  Member "syntax enhanced neural machine translation with syntax aware word representations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "is pos tagging necessary or even helpful for neural dependency parsing")
  Member "inducing grammars with and for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised recurrent neural network grammars")
  Member "self attention with relative position representations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "non autoregressive neural dialogue generation")
  Member "why self attention a targeted evaluation of neural machine translation architectures":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "meta embeddings based on self attention")
  Member "modeling localness for self attention networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gret global representation enhanced transformer")
  Member "the importance of being recurrent for modeling hierarchical structure":
    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)