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: "document level neural machine translation with hierarchical attention networks"
  Abstract: Neural Machine Translation (NMT) can be improved by including document-level contextual information. For this purpose, we propose a hierarchical attention model to capture the context in a structured and dynamic manner. The model is integrated in the original NMT architecture as another level of abstraction, conditioning on the NMT model's own previous hidden states. Experiments show that hierarchical attention significantly improves the BLEU score over a strong NMT baseline with the state-of-the-art in context-aware methods, and that both the encoder and decoder benefit from context in complementary ways.

Connected through hyperedge HE-1 (induced by source paper "incorporating bert into neural machine translation"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "towards making the most of bert in neural machine translation"
    - "mass masked sequence to sequence pre training for language generation"
    - "the evolved transformer"
    - "distilling the knowledge of bert for text generation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "roberta a robustly optimized bert pretraining approach"
    - "ms uedin submission to the wmt2018 ape shared task dual source transformer for automatic post editing"

Connected through hyperedge HE-2 (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-3 (induced by source paper "fill in the blanks imputing missing sentences for larger context neural machine translation"):
  with members:
    - "context in neural machine translation a review of models and evaluations"
    - "tagged back translation"
    - "lingvo a modular and scalable framework for sequence to sequence modeling"
    - "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"
    - "a generalized framework of sequence generation with application to undirected sequence models"
    - "achieving human parity on automatic chinese to english news translation"

Connected through hyperedge HE-4 (induced by source paper "context aware learning for neural machine translation"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "has machine translation achieved human parity a case for document level evaluation"
    - "improving the transformer translation model with document level context"
    - "the memad submission to the wmt18 multimodal translation task"
    - "context aware neural machine translation learns anaphora resolution"
    - "learning to discriminate noises for incorporating external information in neural machine translation"
    - "extreme adaptation for personalized neural machine translation"
    - "learning to remember translation history with a continuous cache"

Connected through hyperedge HE-5 (induced by source paper "when and why is document level context useful in neural machine translation"):
  with members:
    - "microsoft translator at wmt 2019 towards large scale document level neural machine translation"
    - "selective attention for context aware neural machine translation"
    - "when a good translation is wrong in context context aware machine translation improves on deixis ellipsis and lexical cohesion"
    - "adaptive attention span in transformers"
    - "has machine translation achieved human parity a case for document level evaluation"
    - "improving the transformer translation model with document level context"
    - "integrating weakly supervised word sense disambiguation into neural machine translation"
    - "context aware neural machine translation learns anaphora resolution"

Connected through hyperedge HE-6 (induced by source paper "reference network for neural machine translation"):
  with members:
    - "towards robust neural machine translation"
    - "adversarial learning with local coordinate coding"
    - "improving the transformer translation model with document level context"
    - "learning to remember translation history with a continuous cache"
    - "attention is all you need"
    - "evaluating discourse phenomena in neural machine translation"
    - "exploiting cross sentence context for neural machine translation"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-7 (induced by source paper "pretrained language models for document level neural machine translation"):
  with members:
    - "transformer xl attentive language models beyond a fixed length context"
    - "improving the transformer translation model with document level context"
    - "context aware neural machine translation learns anaphora resolution"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "modeling coherence for neural machine translation with dynamic and topic caches"
    - "neural machine translation with extended context"
    - "learning to remember translation history with a continuous cache"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "analysing coreference in transformer outputs"):
  with members:
    - "findings of the 2016 wmt shared task on cross lingual pronoun prediction"
    - "microsoft translator at wmt 2019 towards large scale document level neural machine translation"
    - "context aware learning for neural machine translation"
    - "context aware neural machine translation learns anaphora resolution"
    - "neural machine translation with extended context"
    - "learning to remember translation history with a continuous cache"
    - "attention is all you need"
    - "evaluating discourse phenomena in neural machine translation"

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)