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 "improving neural machine translation robustness via data augmentation beyond back translation"):
  Abstract: Neural Machine Translation (NMT) models have been proved strong when translating clean texts, but they are very sensitive to noise in the input. Improving NMT models robustness can be seen as a form of "domain'' adaption to noise. The recently created Machine Translation on Noisy Text task corpus provides noisy-clean parallel data for a few language pairs, but this data is very limited in size and diversity. The state-of-the-art approaches are heavily dependent on large volumes of back-translated data. This paper has two main contributions: Firstly, we propose new data augmentation methods to extend limited noisy data and further improve NMT robustness to noise while keeping the models small. Secondly, we explore the effect of utilizing noise from external data in the form of speech transcripts and show that it could help robustness.

Includes members:
  Member "tagged back translation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "tag less back translation")
  Member "naver labs europe s systems for the wmt19 machine translation robustness task":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "character based nmt with transformer")
  Member "cuni system for the wmt19 robustness task":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "findings of the first shared task on machine translation robustness")
  Member "robust machine translation with domain sensitive pseudo sources baidu osu wmt19 mt robustness shared task system report":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "findings of the first shared task on machine translation robustness")
  Member "findings of the first shared task on machine translation robustness":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "flaubert unsupervised language model pre training for french")
  Member "training on synthetic noise improves robustness to natural noise in machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust unsupervised neural machine translation with adversarial training")
  Member "robust neural machine translation with doubly adversarial inputs":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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-8 (induced by source paper "robust unsupervised neural machine translation with adversarial training")

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)