human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "character based nmt with transformer"):
  with members:
    - "learning deep transformer models for machine translation"
    - "improving robustness of machine translation with synthetic noise"
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "revisiting character based neural machine translation with capacity and compression"
    - "learning to segment inputs for nmt favors character level processing"
    - "training deeper neural machine translation models with transparent attention"
    - "character level language modeling with deeper self attention"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-2 (induced by source paper "findings of the first shared task on machine translation robustness"):
  with members:
    - "improving robustness of machine translation with synthetic noise"
    - "ntt s machine translation systems for wmt19 robustness task"
    - "on evaluation of adversarial perturbations for sequence to sequence models"
    - "cuni system for the wmt19 robustness task"
    - "robust machine translation with domain sensitive pseudo sources baidu osu wmt19 mt robustness shared task system report"
    - "compare mt a tool for holistic comparison of language generation systems"
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "robust neural machine translation with doubly adversarial inputs"

Connected through hyperedge HE-3 (induced by source paper "machine translation of restaurant reviews new corpus for domain adaptation and robustness"):
  with members:
    - "tagged back translation"
    - "improving robustness of machine translation with synthetic noise"
    - "findings of the first shared task on machine translation robustness"
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "scaling neural machine translation"
    - "a call for clarity in reporting bleu scores"
    - "mtnt a testbed for machine translation of noisy text"

Connected through hyperedge HE-4 (induced by source paper "improving neural machine translation robustness via data augmentation beyond back translation"):
  with members:
    - "tagged back translation"
    - "cuni system for the wmt19 robustness task"
    - "robust machine translation with domain sensitive pseudo sources baidu osu wmt19 mt robustness shared task system report"
    - "findings of the first shared task on machine translation robustness"
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "robust neural machine translation with doubly adversarial inputs"
    - "towards robust neural machine translation"
    - "mtnt a testbed for machine translation of noisy text"

Connected through hyperedge HE-5 (induced by source paper "naver labs europe s systems for the document level generation and translation task at wngt 2019"):
  with members:
    - "facebook fair s wmt19 news translation task submission"
    - "findings of the third workshop on neural generation and translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "scaling neural machine translation"
    - "understanding back translation at scale"
    - "data to text generation with content selection and planning"
    - "attention is all you need"
    - "challenges in data to document generation"

Question: Please tell me which class the center node 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)