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: "on the impact of various types of noise on neural machine translation"
  Abstract: We examine how various types of noise in the parallel training data impact the quality of neural machine translation systems. We create five types of artificial noise and analyze how they degrade performance in neural and statistical machine translation. We find that neural models are generally more harmed by noise than statistical models. For one especially egregious type of noise they learn to just copy the input sentence.

Connected through hyperedge HE-1 (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-2 (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"
    - "attaining the unattainable reassessing claims of human parity in 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-3 (induced by source paper "unsupervised neural machine translation with smt as posterior regularization"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "regularizing neural machine translation by target bidirectional agreement"
    - "prior knowledge integration for neural machine translation using posterior regularization"
    - "joint training for neural machine translation models with monolingual data"
    - "phrase based neural unsupervised machine translation"
    - "unsupervised neural machine translation with weight sharing"
    - "unsupervised machine translation using monolingual corpora only"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "dynamically composing domain data selection with clean data selection by co curricular learning for neural machine translation"):
  with members:
    - "multilingual neural machine translation with knowledge distillation"
    - "reinforcement learning based curriculum optimization for neural machine translation"
    - "curriculum learning for domain adaptation in neural machine translation"
    - "competence based curriculum learning for neural machine translation"
    - "freezing subnetworks to analyze domain adaptation in neural machine translation"
    - "denoising neural machine translation training with trusted data and online data selection"
    - "an empirical exploration of curriculum learning for neural machine translation"
    - "subword regularization improving neural network translation models with multiple subword candidates"

Connected through hyperedge HE-5 (induced by source paper "automatic testing and improvement of machine translation"):
  with members:
    - "metamorphic testing a new approach for generating next test cases"
    - "machine learning testing survey landscapes and horizons"
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "modeling recurrence for transformer"
    - "robust neural machine translation with doubly adversarial inputs"
    - "towards robust neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "guiding neural machine translation with retrieved translation pieces"

Connected through hyperedge HE-6 (induced by source paper "membership inference attacks on sequence to sequence models"):
  with members:
    - "towards demystifying membership inference attacks"
    - "algorithms that remember model inversion attacks and data protection law"
    - "a call for clarity in reporting bleu scores"
    - "ml leaks model and data independent membership inference attacks and defenses on machine learning models"
    - "understanding membership inferences on well generalized learning models"
    - "knock knock who s there membership inference on aggregate location data"
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "improving robustness of machine translation with synthetic noise"):
  with members:
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "mtnt a testbed for machine translation of noisy text"
    - "synthetic and natural noise both break neural machine translation"
    - "regularization techniques for fine tuning in neural machine translation"
    - "six challenges for neural machine translation"
    - "domain control for neural machine translation"
    - "using the output embedding to improve language models"
    - "robsut wrod reocginiton via semi character recurrent neural network"

Connected through hyperedge HE-8 (induced by source paper "cuni system for the wmt19 robustness task"):
  with members:
    - "improving robustness of machine translation with synthetic noise"
    - "findings of the first shared task on machine translation robustness"
    - "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)