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: "attaining the unattainable reassessing claims of human parity in neural machine translation"
  Abstract: We reassess a recent study (Hassan et al., 2018) that claimed that machine translation (MT) has reached human parity for the translation of news from Chinese into English, using pairwise ranking and considering three variables that were not taken into account in that previous study: the language in which the source side of the test set was originally written, the translation proficiency of the evaluators, and the provision of inter-sentential context. If we consider only original source text (i.e. not translated from another language, or translationese), then we find evidence showing that human parity has not been achieved. We compare the judgments of professional translators against those of non-experts and discover that those of the experts result in higher inter-annotator agreement and better discrimination between human and machine translations. In addition, we analyse the human translations of the test set and identify important translation issues. Finally, based on these findings, we provide a set of recommendations for future human evaluations of MT.

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 "context in neural machine translation a review of models and evaluations"):
  with members:
    - "assessing crosslingual discourse relations in machine translation"
    - "pronoun translation in english french machine translation an analysis of error types"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "marian fast neural machine translation in c"
    - "automatic reference based evaluation of pronoun translation misses the point"
    - "context aware neural machine translation learns anaphora resolution"
    - "a comparison of transformer and recurrent neural networks on multilingual neural machine translation"
    - "a large scale test set for the evaluation of context aware pronoun translation in neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "naver labs europe s systems for the wmt19 machine translation robustness task"):
  with members:
    - "improving robustness of machine translation with synthetic noise"
    - "findings of the first shared task on machine translation robustness"
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "on the impact of various types of noise on neural machine translation"
    - "towards robust neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "scaling neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "on the evaluation of machine translation systems trained with back translation"):
  with members:
    - "facebook fair s wmt19 news translation task submission"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "tagged back translation"
    - "the effect of translationese in machine translation test sets"
    - "bertscore evaluating text generation with bert"
    - "text repair model for neural machine translation"
    - "translationese in machine translation evaluation"
    - "investigating backtranslation in neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "translationese as a language in multilingual nmt"):
  with members:
    - "post editese an exacerbated translationese"
    - "tagged back translation"
    - "the effect of translationese in machine translation test sets"
    - "ape at scale and its implications on mt evaluation biases"
    - "controlling text complexity in neural machine translation"
    - "on the evaluation of machine translation systems trained with back translation"
    - "translationese in machine translation evaluation"
    - "a call for clarity in reporting bleu scores"

Connected through hyperedge HE-6 (induced by source paper "post editing productivity with neural machine translation an empirical assessment of speed and quality in the banking and finance domain"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "has machine translation achieved human parity a case for document level evaluation"
    - "the university of edinburgh s neural mt systems for wmt17"
    - "is neural machine translation ready for deployment a case study on 30 translation directions"
    - "automatic tm cleaning through mt and pos tagging autodesk s submission to the nlp4tm 2016 shared task"
    - "edinburgh neural machine translation systems for wmt 16"
    - "neural machine translation of rare words with subword units"
    - "neural machine translation by jointly learning to align and translate"

Connected through hyperedge HE-7 (induced by source paper "english czech systems in wmt19 document level transformer"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "marian fast neural machine translation in c"
    - "a call for clarity in reporting bleu scores"
    - "adafactor adaptive learning rates with sublinear memory cost"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "post editese an exacerbated translationese"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "a multifaceted evaluation of neural versus phrase based machine translation for 9 language directions"

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)