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 neural machine translation with dynamic and topic caches"
  Abstract: Sentences in a well-formed text are connected to each other via various links to form the cohesive structure of the text. Current neural machine translation (NMT) systems translate a text in a conventional sentence-by-sentence fashion, ignoring such cross-sentence links and dependencies. This may lead to generate an incoherent target text for a coherent source text. In order to handle this issue, we propose a cache-based approach to modeling coherence for neural machine translation by capturing contextual information either from recently translated sentences or the entire document. Particularly, we explore two types of caches: a dynamic cache, which stores words from the best translation hypotheses of preceding sentences, and a topic cache, which maintains a set of target-side topical words that are semantically related to the document to be translated. On this basis, we build a new layer to score target words in these two caches with a cache-based neural model. Here the estimated probabilities from the cache-based neural model are combined with NMT probabilities into the final word prediction probabilities via a gating mechanism. Finally, the proposed cache-based neural model is trained jointly with NMT system in an end-to-end manner. Experiments and analysis presented in this paper demonstrate that the proposed cache-based model achieves substantial improvements over several state-of-the-art SMT and NMT baselines.

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"
    - "modeling coherence for discourse neural machine translation"

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"
    - "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-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 "acquiring knowledge from pre trained model to neural machine translation"):
  with members:
    - "multi layer representation fusion for neural machine translation"
    - "learning representation mapping for relation detection in knowledge base question answering"
    - "mass masked sequence to sequence pre training for language generation"
    - "dynamic past and future for neural machine translation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "cross lingual language model pretraining"
    - "investigating backtranslation in neural machine translation"
    - "exploiting deep representations for neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "selective attention for context aware neural machine translation"):
  with members:
    - "has machine translation achieved human parity a case for document level evaluation"
    - "improving the transformer translation model with document level context"
    - "context aware neural machine translation learns anaphora resolution"
    - "contextual neural model for translating bilingual multi speaker conversations"
    - "sparse and constrained attention for neural machine translation"
    - "a large scale test set for the evaluation of context aware pronoun translation in neural machine translation"
    - "neural machine translation with extended context"
    - "learning to remember translation history with a continuous cache"

Connected through hyperedge HE-6 (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"
    - "document level neural machine translation with hierarchical attention networks"

Connected through hyperedge HE-7 (induced by source paper "document level neural machine translation with inter sentence attention"):
  with members:
    - "semantic role labeling with associated memory network"
    - "improving the transformer translation model with document level context"
    - "context aware neural machine translation learns anaphora resolution"
    - "extreme adaptation for personalized neural machine translation"
    - "thumt an open source toolkit for neural machine translation"
    - "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 "context aware monolingual repair for neural machine translation"):
  with members:
    - "ape at scale and its implications on mt evaluation biases"
    - "when a good translation is wrong in context context aware machine translation improves on deixis ellipsis and lexical cohesion"
    - "achieving human parity on automatic chinese to english news translation"
    - "modeling coherence for discourse neural machine translation"
    - "training tips for the transformer model"
    - "context aware neural machine translation learns anaphora resolution"
    - "neural machine translation with extended context"
    - "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)