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 "findings of the first shared task on machine translation robustness"):
  Abstract: We share the findings of the first shared task on improving robustness of Machine Translation (MT). The task provides a testbed representing challenges facing MT models deployed in the real world, and facilitates new approaches to improve models; robustness to noisy input and domain mismatch. We focus on two language pairs (English-French and English-Japanese), and the submitted systems are evaluated on a blind test set consisting of noisy comments on Reddit and professionally sourced translations. As a new task, we received 23 submissions by 11 participating teams from universities, companies, national labs, etc. All submitted systems achieved large improvements over baselines, with the best improvement having +22.33 BLEU. We evaluated submissions by both human judgment and automatic evaluation (BLEU), which shows high correlations (Pearson's r = 0.94 and 0.95). Furthermore, we conducted a qualitative analysis of the submitted systems using compare-mt, which revealed their salient differences in handling challenges in this task. Such analysis provides additional insights when there is occasional disagreement between human judgment and BLEU, e.g. systems better at producing colloquial expressions received higher score from human judgment.

Includes members:
  Member "naver labs europe s systems for the wmt19 machine translation robustness task":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "character based nmt with transformer")
  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 "ntt s machine translation systems for wmt19 robustness task":
    also connected through other hyperedges:
      (none)
  Member "on evaluation of adversarial perturbations for sequence to sequence models":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adv bert bert is not robust on misspellings generating nature adversarial samples on bert")
  Member "cuni system for the wmt19 robustness task":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improving neural machine translation robustness via data augmentation beyond back translation")
  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-5 (induced by source paper "improving neural machine translation robustness via data augmentation beyond back translation")
  Member "compare mt a tool for holistic comparison of language generation systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "domain differential adaptation for neural machine translation")
  Member "training on synthetic noise improves robustness to natural noise in machine translation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)