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: "hybrid self attention network for machine translation"
  Abstract: The encoder-decoder is the typical framework for Neural Machine Translation (NMT), and different structures have been developed for improving the translation performance. Transformer is one of the most promising structures, which can leverage the self-attention mechanism to capture the semantic dependency from global view. However, it cannot distinguish the relative position of different tokens very well, such as the tokens located at the left or right of the current token, and cannot focus on the local information around the current token either. To alleviate these problems, we propose a novel attention mechanism named Hybrid Self-Attention Network (HySAN) which accommodates some specific-designed masks for self-attention network to extract various semantic, such as the global/local information, the left/right part context. Finally, a squeeze gate is introduced to combine different kinds of SANs for fusion. Experimental results on three machine translation tasks show that our proposed framework outperforms the Transformer baseline significantly and achieves superior results over state-of-the-art NMT systems.

Connected through hyperedge HE-1 (induced by source paper "controllable time delay transformer for real time punctuation prediction and disfluency detection"):
  with members:
    - "fast and accurate capitalization and punctuation for automatic speech recognition using transformer and chunk merging"
    - "multi task self supervised learning for disfluency detection"
    - "stacl simultaneous translation with implicit anticipation and controllable latency using prefix to prefix framework"
    - "disfluency detection using a noisy channel model and a deep neural language model"
    - "attention is all you need"
    - "deep semantic role labeling with self attention"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "hard but robust easy but sensitive how encoder and decoder perform in neural machine translation"):
  with members:
    - "mass masked sequence to sequence pre training for language generation"
    - "universal transformers"
    - "achieving human parity on automatic chinese to english news translation"
    - "beyond error propagation in neural machine translation characteristics of language also matter"
    - "non autoregressive neural machine translation with enhanced decoder input"
    - "evaluating layers of representation in neural machine translation on part of speech and semantic tagging tasks"
    - "sharp nearby fuzzy far away how neural language models use context"
    - "dense information flow for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "self attentional models for lattice inputs"):
  with members:
    - "exploiting semantics in neural machine translation with graph convolutional networks"
    - "graph to sequence learning using gated graph neural networks"
    - "graph convolutional networks for named entity recognition"
    - "attention is all you need"
    - "on the fly operation batching in dynamic computation graphs"
    - "graph convolutional encoders for syntax aware neural machine translation"
    - "convolutional sequence to sequence learning"
    - "a structured self attentive sentence embedding"

Connected through hyperedge HE-4 (induced by source paper "empirical evaluation of sequence to sequence models for word discovery in low resource settings"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "adding interpretable attention to neural translation models improves word alignment"
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "pervasive attention 2d convolutional neural networks for sequence to sequence prediction"
    - "multi head attention with disagreement regularization"
    - "a case study on using speech to translation alignments for language documentation"
    - "attention is all you need"

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)