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: "tagged back translation"
  Abstract: Recent work in Neural Machine Translation (NMT) has shown significant quality gains from noised-beam decoding during back-translation, a method to generate synthetic parallel data. We show that the main role of such synthetic noise is not to diversify the source side, as previously suggested, but simply to indicate to the model that the given source is synthetic. We propose a simpler alternative to noising techniques, consisting of tagging back-translated source sentences with an extra token. Our results on WMT outperform noised back-translation in English-Romanian and match performance on English-German, re-defining state-of-the-art in the former.

Connected through hyperedge HE-1 (induced by source paper "tag less back translation"):
  with members:
    - "using monolingual data in neural machine translation a systematic study"
    - "generalizing back translation in neural machine translation"
    - "adaptation of machine translation models with back translated data using transductive data selection methods"
    - "pay less attention with lightweight and dynamic convolutions"
    - "fixynn efficient hardware for mobile computer vision via transfer learning"
    - "investigating backtranslation in neural machine translation"
    - "back translation sampling by targeting difficult words in neural machine translation"
    - "trivial transfer learning for low resource neural machine translation"

Connected through hyperedge HE-2 (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"
    - "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"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"

Connected through hyperedge HE-3 (induced by source paper "iterative batch back translation for neural machine translation a conceptual model"):
  with members:
    - "using monolingual data in neural machine translation a systematic study"
    - "generalizing back translation in neural machine translation"
    - "adaptation of machine translation models with back translated data using transductive data selection methods"
    - "pay less attention with lightweight and dynamic convolutions"
    - "investigating backtranslation in neural machine translation"
    - "back translation sampling by targeting difficult words in neural machine translation"
    - "scaling neural machine translation"
    - "joint training for neural machine translation models with monolingual data"

Connected through hyperedge HE-4 (induced by source paper "machine translation of restaurant reviews new corpus for domain adaptation and robustness"):
  with members:
    - "naver labs europe s systems for the wmt19 machine translation robustness task"
    - "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"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "scaling neural machine translation"
    - "a call for clarity in reporting bleu scores"
    - "mtnt a testbed for machine translation of noisy text"

Connected through hyperedge HE-5 (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"
    - "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"
    - "attaining the unattainable reassessing claims of human parity in neural machine translation"
    - "investigating backtranslation in neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "domain translationese and noise in synthetic data for neural machine translation"):
  with members:
    - "the effect of translationese in machine translation test sets"
    - "using monolingual data in neural machine translation a systematic study"
    - "the source target domain mismatch problem in machine translation"
    - "ape at scale and its implications on mt evaluation biases"
    - "on the evaluation of machine translation systems trained with back translation"
    - "translationese in machine translation evaluation"
    - "marian fast neural machine translation in c"
    - "a call for clarity in reporting bleu scores"

Connected through hyperedge HE-7 (induced by source paper "improving neural machine translation robustness via data augmentation beyond back translation"):
  with members:
    - "naver labs europe s systems for the wmt19 machine translation robustness task"
    - "cuni system for the wmt19 robustness task"
    - "robust machine translation with domain sensitive pseudo sources baidu osu wmt19 mt robustness shared task system report"
    - "findings of the first shared task on machine translation robustness"
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "robust neural machine translation with doubly adversarial inputs"
    - "towards robust neural machine translation"
    - "mtnt a testbed for machine translation of noisy text"

Connected through hyperedge HE-8 (induced by source paper "facebook ai s wat19 myanmar english translation task submission"):
  with members:
    - "facebook fair s wmt19 news translation task submission"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "simple and effective noisy channel modeling for neural machine translation"
    - "the source target domain mismatch problem in machine translation"
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "revisiting self training for neural sequence generation"
    - "roberta a robustly optimized bert pretraining approach"
    - "achieving human parity on automatic chinese to english news 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)