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: "compare mt a tool for holistic comparison of language generation systems"
  Abstract: In this paper, we describe compare-mt, a tool for holistic analysis and comparison of the results of systems for language generation tasks such as machine translation. The main goal of the tool is to give the user a high-level and coherent view of the salient differences between systems that can then be used to guide further analysis or system improvement. It implements a number of tools to do so, such as analysis of accuracy of generation of particular types of words, bucketed histograms of sentence accuracies or counts based on salient characteristics, and extraction of characteristic $n$-grams for each system. It also has a number of advanced features such as use of linguistic labels, source side data, or comparison of log likelihoods for probabilistic models, and also aims to be easily extensible by users to new types of analysis. The code is available at this https URL

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"
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "robust neural machine translation with doubly adversarial inputs"

Connected through hyperedge HE-2 (induced by source paper "domain differential adaptation for neural machine translation"):
  with members:
    - "domain adaptation of neural machine translation by lexicon induction"
    - "non parametric adaptation for neural machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "unsupervised transfer learning for spoken language understanding in intelligent agents"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"

Connected through hyperedge HE-3 (induced by source paper "beyond bleu training neural machine translation with semantic similarity"):
  with members:
    - "simple and effective paraphrastic similarity from parallel translations"
    - "unsupervised learning of sentence embeddings using compositional n gram features"
    - "supervised learning of universal sentence representations from natural language inference data"
    - "paranmt 50m pushing the limits of paraphrastic sentence embeddings with millions of machine translations"
    - "classical structured prediction losses for sequence to sequence learning"
    - "convolutional sequence to sequence learning"
    - "revisiting recurrent networks for paraphrastic sentence embeddings"
    - "regularizing neural networks by penalizing confident output distributions"

Connected through hyperedge HE-4 (induced by source paper "mask predict parallel decoding of conditional masked language models"):
  with members:
    - "insertion based decoding with automatically inferred generation order"
    - "insertion transformer flexible sequence generation via insertion operations"
    - "pay less attention with lightweight and dynamic convolutions"
    - "mass masked sequence to sequence pre training for language generation"
    - "bert has a mouth and it must speak bert as a markov random field language model"
    - "cross lingual language model pretraining"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "bert pre training of deep bidirectional transformers for language understanding"

Connected through hyperedge HE-5 (induced by source paper "an analysis of source side grammatical errors in nmt"):
  with members:
    - "improving robustness of machine translation with synthetic noise"
    - "reference less quality estimation of text simplification systems"
    - "on evaluation of adversarial perturbations for sequence to sequence models"
    - "towards robust neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "neural machine translation of text from non native speakers"
    - "understanding back translation at scale"
    - "jfleg a fluency corpus and benchmark for grammatical error correction"

Connected through hyperedge HE-6 (induced by source paper "unsupervised domain adaptation for neural machine translation with domain aware feature embeddings"):
  with members:
    - "non parametric adaptation for neural machine translation"
    - "a survey of domain adaptation for neural machine translation"
    - "the best of both worlds combining recent advances in neural machine translation"
    - "attention is all you need"
    - "an empirical comparison of simple domain adaptation methods for neural machine translation"
    - "convolutional sequence to sequence learning"
    - "six challenges for neural machine translation"
    - "domain control for neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "vizseq a visual analysis toolkit for text generation tasks"):
  with members:
    - "bertscore evaluating text generation with bert"
    - "opennmt neural machine translation toolkit"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "relevance of unsupervised metrics in task oriented dialogue for evaluating natural language generation"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "google s neural machine translation system bridging the gap between human and machine translation"
    - "cider consensus based image description evaluation"

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)