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: "has machine translation achieved human parity a case for document level evaluation"
  Abstract: Recent research suggests that neural machine translation achieves parity with professional human translation on the WMT Chinese--English news translation task. We empirically test this claim with alternative evaluation protocols, contrasting the evaluation of single sentences and entire documents. In a pairwise ranking experiment, human raters assessing adequacy and fluency show a stronger preference for human over machine translation when evaluating documents as compared to isolated sentences. Our findings emphasise the need to shift towards document-level evaluation as machine translation improves to the degree that errors which are hard or impossible to spot at the sentence-level become decisive in discriminating quality of different translation outputs.

Connected through hyperedge HE-1 (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-2 (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"
    - "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-3 (induced by source paper "towards understanding neural machine translation with word importance"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "assessing the ability of self attention networks to learn word order"
    - "attention is not explanation"
    - "train sort explain learning to diagnose translation models"
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "achieving human parity on automatic chinese to english news translation"
    - "how important is a neuron"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "selective attention for context aware neural machine translation"):
  with members:
    - "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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "context aware learning for neural machine translation"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "improving the transformer translation model with document level context"
    - "document level neural machine translation with hierarchical attention networks"
    - "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-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"
    - "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"
    - "context aware neural machine translation learns anaphora resolution"

Connected through hyperedge HE-7 (induced by source paper "pitfalls in the evaluation of sentence embeddings"):
  with members:
    - "no training required exploring random encoders for sentence classification"
    - "universal sentence encoder"
    - "a call for clarity in reporting bleu scores"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "what you can cram into a single vector probing sentence embeddings for linguistic properties"
    - "evaluation of sentence embeddings in downstream and linguistic probing tasks"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "senteval an evaluation toolkit for universal sentence representations"

Connected through hyperedge HE-8 (induced by source paper "evaluating pronominal anaphora in machine translation an evaluation measure and a test suite"):
  with members:
    - "findings of the 2016 wmt shared task on cross lingual pronoun prediction"
    - "pairwise neural machine translation evaluation"
    - "achieving human parity on automatic chinese to english news translation"
    - "automatic reference based evaluation of pronoun translation misses the point"
    - "deep contextualized word representations"
    - "context aware neural machine translation learns anaphora resolution"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a large scale test set for the evaluation of context aware pronoun translation 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)