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: "training on synthetic noise improves robustness to natural noise in machine translation"
  Abstract: We consider the problem of making machine translation more robust to character-level variation at the source side, such as typos. Existing methods achieve greater coverage by applying subword models such as byte-pair encoding (BPE) and character-level encoders, but these methods are highly sensitive to spelling mistakes. We show how training on a mild amount of random synthetic noise can dramatically improve robustness to these variations, without diminishing performance on clean text. We focus on translation performance on natural noise, as captured by frequent corrections in Wikipedia edit logs, and show that robustness to such noise can be achieved using a balanced diet of simple synthetic noises at training time, without access to the natural noise data or distribution.

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"
    - "cross lingual language model pretraining"
    - "robust neural machine translation with doubly adversarial inputs"
    - "towards robust neural machine translation"
    - "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 "robustness to programmable string transformations via augmented abstract training"):
  with members:
    - "popqorn quantifying robustness of recurrent neural networks"
    - "combating adversarial misspellings with robust word recognition"
    - "adversarial attacks on deep learning models in natural language processing a survey"
    - "adversarial example generation with syntactically controlled paraphrase networks"
    - "generating natural adversarial examples"
    - "robsut wrod reocginiton via semi character recurrent neural network"
    - "adam a method for stochastic optimization"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-3 (induced by source paper "unlearn dataset bias in natural language inference by fitting the residual"):
  with members:
    - "right for the wrong reasons diagnosing syntactic heuristics in natural language inference"
    - "selection bias explorations and debias methods for natural language sentence matching datasets"
    - "inoculation by fine tuning a method for analyzing challenge datasets"
    - "don t take the premise for granted mitigating artifacts in natural language inference"
    - "don t take the easy way out ensemble based methods for avoiding known dataset biases"
    - "paws paraphrase adversaries from word scrambling"
    - "theoretically principled trade off between robustness and accuracy"
    - "learning robust representations by projecting superficial statistics out"

Connected through hyperedge HE-4 (induced by source paper "character based nmt with transformer"):
  with members:
    - "naver labs europe s systems for the wmt19 machine translation robustness task"
    - "learning deep transformer models for machine translation"
    - "improving robustness of machine translation with synthetic noise"
    - "revisiting character based neural machine translation with capacity and compression"
    - "learning to segment inputs for nmt favors character level processing"
    - "training deeper neural machine translation models with transparent attention"
    - "character level language modeling with deeper self attention"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-5 (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"
    - "robust neural machine translation with doubly adversarial inputs"

Connected through hyperedge HE-6 (induced by source paper "robust neural machine translation with doubly adversarial inputs"):
  with members:
    - "towards robust neural 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 "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"
    - "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-8 (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"
    - "findings of the first shared task on machine translation robustness"
    - "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"

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)