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: "findings of the first shared task on machine translation robustness"
  Abstract: We share the findings of the first shared task on improving robustness of Machine Translation (MT). The task provides a testbed representing challenges facing MT models deployed in the real world, and facilitates new approaches to improve models; robustness to noisy input and domain mismatch. We focus on two language pairs (English-French and English-Japanese), and the submitted systems are evaluated on a blind test set consisting of noisy comments on Reddit and professionally sourced translations. As a new task, we received 23 submissions by 11 participating teams from universities, companies, national labs, etc. All submitted systems achieved large improvements over baselines, with the best improvement having +22.33 BLEU. We evaluated submissions by both human judgment and automatic evaluation (BLEU), which shows high correlations (Pearson's r = 0.94 and 0.95). Furthermore, we conducted a qualitative analysis of the submitted systems using compare-mt, which revealed their salient differences in handling challenges in this task. Such analysis provides additional insights when there is occasional disagreement between human judgment and BLEU, e.g. systems better at producing colloquial expressions received higher score from human judgment.

Connected through hyperedge HE-1 (induced by source paper "flaubert unsupervised language model pre training for french"):
  with members:
    - "arabert transformer based model for arabic language understanding"
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "exploring the limits of transfer learning with a unified text to text transformer"
    - "reducing transformer depth on demand with structured dropout"
    - "very deep self attention networks for end to end speech recognition"
    - "unsupervised cross lingual representation learning at scale"
    - "camembert a tasty french language model"
    - "why deep transformers are difficult to converge from computation order to lipschitz restricted parameter initialization"

Connected through hyperedge HE-2 (induced by source paper "robust machine translation with domain sensitive pseudo sources baidu osu wmt19 mt robustness shared task system report"):
  with members:
    - "simultaneous translation with flexible policy via restricted imitation learning"
    - "improving robustness of machine translation with synthetic noise"
    - "end to end speech translation with knowledge distillation"
    - "a survey of domain adaptation for neural machine translation"
    - "switchout an efficient data augmentation algorithm for neural machine translation"
    - "robust neural machine translation with joint textual and phonetic embedding"
    - "multi reference training with pseudo references for neural translation and text generation"
    - "ensemble sequence level training for multimodal mt osu baidu wmt18 multimodal machine translation system report"

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"
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "attaining the unattainable reassessing claims of human parity in neural 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 "ntt s machine translation systems for wmt19 robustness task"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "scaling neural machine translation"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "mtnt a testbed for machine translation of noisy text"
    - "neural machine translation with extended context"
    - "attention is all you need"
    - "evaluating discourse phenomena in neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "machine translation of restaurant reviews new corpus for domain adaptation and robustness"):
  with members:
    - "tagged back translation"
    - "naver labs europe s systems for the wmt19 machine translation robustness task"
    - "improving robustness of machine translation with synthetic noise"
    - "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-6 (induced by source paper "improving neural machine translation robustness via data augmentation beyond back translation"):
  with members:
    - "tagged back translation"
    - "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"
    - "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-7 (induced by source paper "cuni system for the wmt19 robustness task"):
  with members:
    - "improving robustness of machine translation with synthetic noise"
    - "on the impact of various types of noise on neural machine translation"
    - "towards robust neural machine translation"
    - "training tips for the transformer model"
    - "robust neural machine translation with joint textual and phonetic embedding"
    - "mtnt a testbed for machine translation of noisy text"
    - "attention is all you need"
    - "synthetic and natural noise both break 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)