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 "naver labs europe s systems for the wmt19 machine translation robustness task"):
  Abstract: This paper describes the systems that we submitted to the WMT19 Machine Translation robustness task. This task aims to improve MT's robustness to noise found on social media, like informal language, spelling mistakes and other orthographic variations. The organizers provide parallel data extracted from a social media website in two language pairs: French-English and Japanese-English (in both translation directions). The goal is to obtain the best scores on unseen test sets from the same source, according to automatic metrics (BLEU) and human evaluation. We proposed one single and one ensemble system for each translation direction. Our ensemble models ranked first in all language pairs, according to BLEU evaluation. We discuss the pre-processing choices that we made, and present our solutions for robustness to noise and domain adaptation.

Includes members:
  Member "improving robustness of machine translation with synthetic noise":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (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-3 (induced by source paper "robust unsupervised neural machine translation with adversarial training")
  Member "attaining the unattainable reassessing claims of human parity in neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on document level machine translation methods and evaluation")
  Member "on the impact of various types of noise on neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "findings of the first shared task on machine translation robustness")
  Member "towards robust neural machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust unsupervised neural machine translation with adversarial training")
  Member "achieving human parity on automatic chinese to english news translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gret global representation enhanced transformer")
  Member "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multilingual denoising pre training 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)