human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "robust unsupervised neural machine translation with adversarial training"):
  Abstract: Unsupervised neural machine translation (UNMT) has recently attracted great interest in the machine translation community, achieving only slightly worse results than supervised neural machine translation. However, in real-world scenarios, there usually exists minor noise in the input sentence and the neural translation system is sensitive to the small perturbations in the input, leading to poor performance. In this paper, we first define two types of noises and empirically show the effect of these noisy data on UNMT performance. Moreover, we propose adversarial training methods to improve the robustness of UNMT in the noisy scenario. To the best of our knowledge, this paper is the first work to explore the robustness of UNMT. Experimental results on several language pairs show that our proposed methods substantially outperform conventional UNMT systems in the noisy scenario.

Includes members:
  Member "mass masked sequence to sequence pre training for language generation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on contextual embeddings")
  Member "extract and edit an alternative to back translation for unsupervised neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multilingual denoising pre training for neural machine translation")
  Member "training on synthetic noise improves robustness to natural noise in machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robustness to programmable string transformations via augmented abstract training")
  Member "cross lingual language model pretraining":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on contextual embeddings")
  Member "robust neural machine translation with doubly adversarial inputs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "freelb enhanced adversarial training for natural language understanding")
  Member "towards robust neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "assessing the ability of self attention networks to learn word order")
  Member "robust neural machine translation with joint textual and phonetic embedding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robust neural machine translation with doubly adversarial inputs")
  Member "unsupervised neural machine translation with weight sharing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mass masked sequence to sequence pre training for language generation")

Question: Please tell me which class the source paper 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)