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: "towards robust neural machine translation"
  Abstract: Small perturbations in the input can severely distort intermediate representations and thus impact translation quality of neural machine translation (NMT) models. In this paper, we propose to improve the robustness of NMT models with adversarial stability training. The basic idea is to make both the encoder and decoder in NMT models robust against input perturbations by enabling them to behave similarly for the original input and its perturbed counterpart. Experimental results on Chinese-English, English-German and English-French translation tasks show that our approaches can not only achieve significant improvements over strong NMT systems but also improve the robustness of NMT models.

Connected through hyperedge HE-1 (induced by source paper "robust unsupervised neural machine translation with adversarial training"):
  with members:
    - "mass masked sequence to sequence pre training for language generation"
    - "extract and edit an alternative to back translation for unsupervised neural machine translation"
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "cross lingual language model pretraining"
    - "robust neural machine translation with doubly adversarial inputs"
    - "robust neural machine translation with joint textual and phonetic embedding"
    - "unsupervised neural machine translation with weight sharing"
    - "unsupervised machine translation using monolingual corpora only"

Connected through hyperedge HE-2 (induced by source paper "assessing the ability of self attention networks to learn word order"):
  with members:
    - "information aggregation for multi head attention with routing by agreement"
    - "what do you learn from context probing for sentence structure in contextualized word representations"
    - "modeling recurrence for transformer"
    - "reinforced self attention network a hybrid of hard and soft attention for sequence modeling"
    - "self attention with relative position representations"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "modeling localness for self attention networks"
    - "deep contextualized word representations"

Connected through hyperedge HE-3 (induced by source paper "syntax enhanced neural machine translation with syntax aware word representations"):
  with members:
    - "deep contextualized word representations"
    - "structural attention neural networks for improved sentiment analysis"
    - "towards bidirectional hierarchical representations for attention based neural machine translation"
    - "attention is all you need"
    - "neural machine translation with source side latent graph parsing"
    - "chunk based bi scale decoder for neural machine translation"
    - "stronger baselines for trustable results in neural machine translation"
    - "modeling past and future for neural machine translation"

Connected through hyperedge HE-4 (induced by source paper "findings of the first shared task on machine translation robustness"):
  with members:
    - "naver labs europe s systems for the wmt19 machine translation robustness task"
    - "improving robustness of machine translation with synthetic noise"
    - "ntt s machine translation systems for wmt19 robustness task"
    - "on evaluation of adversarial perturbations for sequence to sequence models"
    - "cuni system for the wmt19 robustness task"
    - "robust machine translation with domain sensitive pseudo sources baidu osu wmt19 mt robustness shared task system report"
    - "compare mt a tool for holistic comparison of language generation systems"
    - "training on synthetic noise improves robustness to natural noise in machine translation"

Connected through hyperedge HE-5 (induced by source paper "extract and edit an alternative to back translation for unsupervised neural machine translation"):
  with members:
    - "response generation by context aware prototype editing"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "extracting parallel sentences with bidirectional recurrent neural networks to improve machine translation"
    - "phrase based neural unsupervised machine translation"
    - "unsupervised neural machine translation with weight sharing"
    - "generating sentences by editing prototypes"
    - "a challenge set approach to evaluating machine translation"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "robust neural machine translation with doubly adversarial inputs"):
  with members:
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "on adversarial examples for character level neural machine translation"
    - "generating natural language adversarial examples"
    - "robust neural machine translation with joint textual and phonetic embedding"
    - "training deeper neural machine translation models with transparent attention"
    - "mtnt a testbed for machine translation of noisy text"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "understanding back translation at scale"

Connected through hyperedge HE-7 (induced by source paper "dutongchuan context aware translation model for simultaneous interpreting"):
  with members:
    - "ernie enhanced representation through knowledge integration"
    - "attention passing models for robust and data efficient end to end speech translation"
    - "monotonic infinite lookback attention for simultaneous machine translation"
    - "end to end speech translation with knowledge distillation"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "modeling coherence for discourse neural machine translation"
    - "you may not need attention"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "latent part of speech sequences for neural machine translation"):
  with members:
    - "a tree based decoder for neural machine translation"
    - "discrete structural planning for neural machine translation"
    - "top down tree structured decoding with syntactic connections for neural machine translation and parsing"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "learning to parse and translate improves neural machine translation"
    - "syntax directed attention for neural machine translation"
    - "attention is all you need"
    - "stronger baselines for trustable results 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)