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: "modeling coherence for discourse neural machine translation"
  Abstract: Discourse coherence plays an important role in the translation of one text. However, the previous reported models most focus on improving performance over individual sentence while ignoring cross-sentence links and dependencies, which affects the coherence of the text. In this paper, we propose to use discourse context and reward to refine the translation quality from the discourse perspective. In particular, we generate the translation of individual sentences at first. Next, we deliberate the preliminary produced translations, and train the model to learn the policy that produces discourse coherent text by a reward teacher. Practical results on multiple discourse test datasets indicate that our model significantly improves the translation quality over the state-of-the-art baseline system by +1.23 BLEU score. Moreover, our model generates more discourse coherent text and obtains +2.2 BLEU improvements when evaluated by discourse metrics.

Connected through hyperedge HE-1 (induced by source paper "capturing document context inside sentence level neural machine translation models with self training"):
  with members:
    - "putting machine translation in context with the noisy channel model"
    - "microsoft translator at wmt 2019 towards large scale document level neural machine translation"
    - "context aware monolingual repair for neural machine translation"
    - "when a good translation is wrong in context context aware machine translation improves on deixis ellipsis and lexical cohesion"
    - "revisiting self training for neural sequence generation"
    - "fill in the blanks imputing missing sentences for larger context neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "improving the transformer translation model with document level context"

Connected through hyperedge HE-2 (induced by source paper "toward making the most of context in neural machine translation"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "microsoft translator at wmt 2019 towards large scale document level neural machine translation"
    - "selective attention for context aware neural machine translation"
    - "context aware monolingual repair for neural machine translation"
    - "improving the transformer translation model with document level context"
    - "self attention with relative position representations"
    - "fusing recency into neural machine translation with an inter sentence gate model"
    - "context aware neural machine translation learns anaphora resolution"

Connected through hyperedge HE-3 (induced by source paper "a survey on document level machine translation methods and evaluation"):
  with members:
    - "context in neural machine translation a review of models and evaluations"
    - "findings of the third workshop on neural generation and translation"
    - "naver labs europe s systems for the document level generation and translation task at wngt 2019"
    - "improving conditioning in context aware sequence to sequence models"
    - "evaluating pronominal anaphora in machine translation an evaluation measure and a test suite"
    - "using whole document context in neural machine translation"
    - "english czech systems in wmt19 document level transformer"
    - "when and why is document level context useful in neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "cued wmt19 ewc lms"):
  with members:
    - "selective attention for context aware neural machine translation"
    - "domain adaptive inference for neural machine translation"
    - "investigating backtranslation in neural machine translation"
    - "has machine translation achieved human parity a case for document level evaluation"
    - "near human level performance in grammatical error correction with hybrid machine translation"
    - "improving the transformer translation model with document level context"
    - "simple fusion return of the language model"
    - "the university of cambridge s machine translation systems for wmt18"

Connected through hyperedge HE-5 (induced by source paper "putting machine translation in context with the noisy channel model"):
  with members:
    - "facebook fair s wmt19 news translation task submission"
    - "simple and effective noisy channel modeling for neural machine translation"
    - "pre trained language model representations for language generation"
    - "an effective approach to unsupervised machine translation"
    - "mass masked sequence to sequence pre training for language generation"
    - "repurposing decoder transformer language models for abstractive summarization"
    - "pretraining based natural language generation for text summarization"
    - "defending against neural fake news"

Connected through hyperedge HE-6 (induced by source paper "dutongchuan context aware translation model for simultaneous interpreting"):
  with members:
    - "ernie enhanced representation through knowledge integration"
    - "attention passing models for robust and data efficient end to end speech translation"
    - "monotonic infinite lookback attention for simultaneous machine translation"
    - "end to end speech translation with knowledge distillation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "towards robust neural machine translation"
    - "you may not need attention"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "incremental transformer with deliberation decoder for document grounded conversations"):
  with members:
    - "knowledge grounded response generation with deep attentional latent variable model"
    - "deepcopy grounded response generation with hierarchical pointer networks"
    - "improving the transformer translation model with document level context"
    - "story ending generation with incremental encoding and commonsense knowledge"
    - "mem2seq effectively incorporating knowledge bases into end to end task oriented dialog systems"
    - "coqa a conversational question answering challenge"
    - "wizard of wikipedia knowledge powered conversational agents"
    - "qanet combining local convolution with global self attention for reading comprehension"

Connected through hyperedge HE-8 (induced by source paper "enhancing context modeling with a query guided capsule network for document level translation"):
  with members:
    - "bridging the gap between training and inference for neural machine translation"
    - "selective attention for context aware neural machine translation"
    - "improving the transformer translation model with document level context"
    - "dtmt a novel deep transition architecture for neural machine translation"
    - "context aware neural machine translation learns anaphora resolution"
    - "towards linear time neural machine translation with capsule networks"
    - "neural machine translation with extended context"
    - "learning to remember translation history with a continuous cache"

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)