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: "exploiting cross sentence context for neural machine translation"
  Abstract: In translation, considering the document as a whole can help to resolve ambiguities and inconsistencies. In this paper, we propose a cross-sentence context-aware approach and investigate the influence of historical contextual information on the performance of neural machine translation (NMT). First, this history is summarized in a hierarchical way. We then integrate the historical representation into NMT in two strategies: 1) a warm-start of encoder and decoder states, and 2) an auxiliary context source for updating decoder states. Experimental results on a large Chinese-English translation task show that our approach significantly improves upon a strong attention-based NMT system by up to +2.1 BLEU points.

Connected through hyperedge HE-1 (induced by source paper "multilingual denoising pre training for neural machine translation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "unsupervised cross lingual representation learning at scale"
    - "improved zero shot neural machine translation via ignoring spurious correlations"
    - "massively multilingual neural machine translation in the wild findings and challenges"
    - "pre trained language model representations for language generation"
    - "ctrl a conditional transformer language model for controllable generation"
    - "mass masked sequence to sequence pre training for language generation"

Connected through hyperedge HE-2 (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"
    - "modeling coherence for discourse neural machine translation"

Connected through hyperedge HE-3 (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"
    - "modeling coherence for discourse 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"

Connected through hyperedge HE-4 (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-5 (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"
    - "modeling coherence for discourse 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"

Connected through hyperedge HE-6 (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-7 (induced by source paper "context aware self attention networks"):
  with members:
    - "neural machine translation with adequacy oriented learning"
    - "achieving human parity on automatic chinese to english news translation"
    - "exploiting deep representations for neural machine translation"
    - "tied multitask learning for neural speech translation"
    - "learning to jointly translate and predict dropped pronouns with a shared reconstruction mechanism"
    - "self attention with relative position representations"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "modeling localness for self attention networks"

Connected through hyperedge HE-8 (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"
    - "modeling coherence for discourse neural machine translation"
    - "you may not need attention"

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)