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 "synchronous bidirectional inference for neural sequence generation"):
  Abstract: In sequence to sequence generation tasks (e.g. machine translation and abstractive summarization), inference is generally performed in a left-to-right manner to produce the result token by token. The neural approaches, such as LSTM and self-attention networks, are now able to make full use of all the predicted history hypotheses from left side during inference, but cannot meanwhile access any future (right side) information and usually generate unbalanced outputs in which left parts are much more accurate than right ones. In this work, we propose a synchronous bidirectional inference model to generate outputs using both left-to-right and right-to-left decoding simultaneously and interactively. First, we introduce a novel beam search algorithm that facilitates synchronous bidirectional decoding. Then, we present the core approach which enables left-to-right and right-to-left decoding to interact with each other, so as to utilize both the history and future predictions simultaneously during inference. We apply the proposed model to both LSTM and self-attention networks. In addition, we propose two strategies for parameter optimization. The extensive experiments on machine translation and abstractive summarization demonstrate that our synchronous bidirectional inference model can achieve remarkable improvements over the strong baselines.

Includes members:
  Member "synchronous bidirectional neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "blank language models")
  Member "pay less attention with lightweight and dynamic convolutions":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on feature normalization and data augmentation")
  Member "bridging the gap between training and inference for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discriminative adversarial search for abstractive summarization")
  Member "asynchronous bidirectional decoding for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "pseudo bidirectional decoding for local sequence transduction")
  Member "regularizing neural machine translation by target bidirectional agreement":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distilling the knowledge of bert for text generation")
  Member "self attention with relative position representations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "non autoregressive neural dialogue generation")
  Member "pervasive attention 2d convolutional neural networks for sequence to sequence prediction":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "neural machine translation with joint representation")
  Member "the best of both worlds combining recent advances in neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "meta embeddings based on self attention")

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)