human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "deconvolution based global decoding for neural machine translation"
  Abstract: A great proportion of sequence-to-sequence (Seq2Seq) models for Neural Machine Translation (NMT) adopt Recurrent Neural Network (RNN) to generate translation word by word following a sequential order. As the studies of linguistics have proved that language is not linear word sequence but sequence of complex structure, translation at each step should be conditioned on the whole target-side context. To tackle the problem, we propose a new NMT model that decodes the sequence with the guidance of its structural prediction of the context of the target sequence. Our model generates translation based on the structural prediction of the target-side context so that the translation can be freed from the bind of sequential order. Experimental results demonstrate that our model is more competitive compared with the state-of-the-art methods, and the analysis reflects that our model is also robust to translating sentences of different lengths and it also reduces repetition with the instruction from the target-side context for decoding.

Connected through hyperedge HE-1 (induced by source paper "gret global representation enhanced transformer"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "context aware self attention networks"
    - "dynamic past and future for neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "exploiting deep representations for neural machine translation"
    - "global encoding for abstractive summarization"
    - "investigating capsule networks with dynamic routing for text classification"
    - "self attention with relative position representations"

Connected through hyperedge HE-2 (induced by source paper "dynamic past and future for neural machine translation"):
  with members:
    - "synchronous bidirectional neural machine translation"
    - "dynamic layer aggregation for neural machine translation with routing by agreement"
    - "information aggregation for multi head attention with routing by agreement"
    - "neural machine translation with adequacy oriented learning"
    - "asynchronous bidirectional decoding for neural machine translation"
    - "investigating capsule networks with dynamic routing for text classification"
    - "information aggregation via dynamic routing for sequence encoding"
    - "a hybrid word character model for abstractive summarization"

Connected through hyperedge HE-3 (induced by source paper "exploiting sentential context for neural machine translation"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "dynamic layer aggregation for neural machine translation with routing by agreement"
    - "information aggregation for multi head attention with routing by agreement"
    - "context aware self attention networks"
    - "universal transformers"
    - "modeling recurrence for transformer"
    - "exploiting deep representations for neural machine translation"
    - "modeling localness for self attention networks"

Connected through hyperedge HE-4 (induced by source paper "simnet stepwise image topic merging network for generating detailed and comprehensive image captions"):
  with members:
    - "neural baby talk"
    - "a skeleton based model for promoting coherence among sentences in narrative story generation"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "unpaired sentiment to sentiment translation a cycled reinforcement learning approach"
    - "improved image captioning via policy gradient optimization of spider"
    - "skeleton key image captioning by skeleton attribute decomposition"
    - "visual translation embedding network for visual relation detection"
    - "semantic compositional networks for visual captioning"

Question: Please tell me which class the center node 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)