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: "mtnt a testbed for machine translation of noisy text"
  Abstract: Noisy or non-standard input text can cause disastrous mistranslations in most modern Machine Translation (MT) systems, and there has been growing research interest in creating noise-robust MT systems. However, as of yet there are no publicly available parallel corpora of with naturally occurring noisy inputs and translations, and thus previous work has resorted to evaluating on synthetically created datasets. In this paper, we propose a benchmark dataset for Machine Translation of Noisy Text (MTNT), consisting of noisy comments on Reddit (www.reddit.com) and professionally sourced translations. We commissioned translations of English comments into French and Japanese, as well as French and Japanese comments into English, on the order of 7k-37k sentences per language pair. We qualitatively and quantitatively examine the types of noise included in this dataset, then demonstrate that existing MT models fail badly on a number of noise-related phenomena, even after performing adaptation on a small training set of in-domain data. This indicates that this dataset can provide an attractive testbed for methods tailored to handling noisy text in MT. The data is publicly available at www.cs.cmu.edu/~pmichel1/mtnt/.

Connected through hyperedge HE-1 (induced by source paper "findings of the first shared task on machine translation robustness"):
  with members:
    - "naver labs europe s systems for the wmt19 machine translation robustness task"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "improved variational neural machine translation by promoting mutual information"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "preventing posterior collapse with delta vaes"
    - "mixture models for diverse machine translation tricks of the trade"
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "variational recurrent neural machine translation"
    - "scaling neural machine translation"
    - "bag of words as target for neural machine translation"
    - "a call for clarity in reporting bleu scores"

Connected through hyperedge HE-3 (induced by source paper "robust neural machine translation with doubly adversarial inputs"):
  with members:
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "towards robust neural machine translation"
    - "on adversarial examples for character level neural machine translation"
    - "generating natural language adversarial examples"
    - "robust neural machine translation with joint textual and phonetic embedding"
    - "training deeper neural machine translation models with transparent attention"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "understanding back translation at scale"

Connected through hyperedge HE-4 (induced by source paper "are sixteen heads really better than one"):
  with members:
    - "analyzing multi head self attention specialized heads do the heavy lifting the rest can be pruned"
    - "transformer xl attentive language models beyond a fixed length context"
    - "weighted transformer network for machine translation"
    - "why self attention a targeted evaluation of neural machine translation architectures"
    - "scaling neural machine translation"
    - "neural network acceptability judgments"
    - "attention is all you need"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-5 (induced by source paper "robust machine translation with domain sensitive pseudo sources baidu osu wmt19 mt robustness shared task system report"):
  with members:
    - "simultaneous translation with flexible policy via restricted imitation learning"
    - "improving robustness of machine translation with synthetic noise"
    - "findings of the first shared task on machine translation robustness"
    - "end to end speech translation with knowledge distillation"
    - "a survey of domain adaptation for neural machine translation"
    - "switchout an efficient data augmentation algorithm for neural machine translation"
    - "robust neural machine translation with joint textual and phonetic embedding"
    - "multi reference training with pseudo references for neural translation and text generation"

Connected through hyperedge HE-6 (induced by source paper "neural machine translation with byte level subwords"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "transformers with convolutional context for asr"
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "language models with transformers"
    - "revisiting character based neural machine translation with capacity and compression"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "when and why are pre trained word embeddings useful for neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "naver labs europe s systems for the wmt19 machine translation robustness task"):
  with members:
    - "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"
    - "attaining the unattainable reassessing claims of human parity in neural machine translation"
    - "on the impact of various types of noise on neural machine translation"
    - "towards robust neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"

Connected through hyperedge HE-8 (induced by source paper "ntt s machine translation systems for wmt19 robustness task"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "findings of the first shared task on machine translation robustness"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "scaling neural machine translation"
    - "subword regularization improving neural network translation models with multiple subword candidates"
    - "neural machine translation with extended context"
    - "attention is all you need"
    - "evaluating discourse phenomena in neural machine translation"

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)