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 machine translation with domain sensitive pseudo sources baidu osu wmt19 mt robustness shared task system report"):
  Abstract: This paper describes the machine translation system developed jointly by Baidu Research and Oregon State University for WMT 2019 Machine Translation Robustness Shared Task. Translation of social media is a very challenging problem, since its style is very different from normal parallel corpora (e.g. News) and also include various types of noises. To make it worse, the amount of social media parallel corpora is extremely limited. In this paper, we use a domain sensitive training method which leverages a large amount of parallel data from popular domains together with a little amount of parallel data from social media. Furthermore, we generate a parallel dataset with pseudo noisy source sentences which are back-translated from monolingual data using a model trained by a similar domain sensitive way. We achieve more than 10 BLEU improvement in both En-Fr and Fr-En translation compared with the baseline methods.

Includes members:
  Member "simultaneous translation with flexible policy via restricted imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning coupled policies for simultaneous machine translation")
  Member "improving robustness of machine translation with synthetic noise":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "character based nmt with transformer")
  Member "findings of the first shared task on machine translation robustness":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "flaubert unsupervised language model pre training for french")
  Member "end to end speech translation with knowledge distillation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multilingual end to end speech translation")
  Member "a survey of domain adaptation for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a brief survey of multilingual neural machine translation")
  Member "switchout an efficient data augmentation algorithm for neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "unsupervised data augmentation for consistency training")
  Member "robust neural machine translation with joint textual and phonetic embedding":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "robust unsupervised neural machine translation with adversarial training")
  Member "multi reference training with pseudo references for neural translation and text generation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "learning to stop in structured prediction for neural machine translation")

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)