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: "facebook fair s wmt19 news translation task submission"
  Abstract: This paper describes Facebook FAIR's submission to the WMT19 shared news translation task. We participate in two language pairs and four language directions, English   German and English   Russian. Following our submission from last year, our baseline systems are large BPE-based transformer models trained with the Fairseq sequence modeling toolkit which rely on sampled back-translations. This year we experiment with different bitext data filtering schemes, as well as with adding filtered back-translated data. We also ensemble and fine-tune our models on domain-specific data, then decode using noisy channel model reranking. Our submissions are ranked first in all four directions of the human evaluation campaign. On En->De, our system significantly outperforms other systems as well as human translations. This system improves upon our WMT'18 submission by 4.5 BLEU points.

Connected through hyperedge HE-1 (induced by source paper "covost a diverse multilingual speech to text translation corpus"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "multilingual end to end speech translation"
    - "harnessing indirect training data for end to end automatic speech translation tricks of the trade"
    - "europarl st a multilingual corpus for speech translation of parliamentary debates"
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "librivoxdeen a corpus for german to english speech translation and speech recognition"
    - "mass a large and clean multilingual corpus of sentence aligned spoken utterances extracted from the bible"
    - "vizseq a visual analysis toolkit for text generation tasks"

Connected through hyperedge HE-2 (induced by source paper "large scale pretraining for neural machine translation with tens of billions of sentence pairs"):
  with members:
    - "mass masked sequence to sequence pre training for language generation"
    - "mixture models for diverse machine translation tricks of the trade"
    - "is word segmentation necessary for deep learning of chinese representations"
    - "cross lingual language model pretraining"
    - "massively multilingual neural machine translation"
    - "hard but robust easy but sensitive how encoder and decoder perform in neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "a tutorial on deep latent variable models of natural language"

Connected through hyperedge HE-3 (induced by source paper "putting machine translation in context with the noisy channel model"):
  with members:
    - "simple and effective noisy channel modeling for neural machine translation"
    - "pre trained language model representations for language generation"
    - "an effective approach to unsupervised machine translation"
    - "mass masked sequence to sequence pre training for language generation"
    - "repurposing decoder transformer language models for abstractive summarization"
    - "pretraining based natural language generation for text summarization"
    - "defending against neural fake news"
    - "transformer xl attentive language models beyond a fixed length context"

Connected through hyperedge HE-4 (induced by source paper "plug and play language models a simple approach to controlled text generation"):
  with members:
    - "simple and effective noisy channel modeling for neural machine translation"
    - "style transformer unpaired text style transfer without disentangled latent representation"
    - "the curious case of neural text degeneration"
    - "ctrl a conditional transformer language model for controllable generation"
    - "transformer xl attentive language models beyond a fixed length context"
    - "fine tuning language models from human preferences"
    - "putting machine translation in context with the noisy channel model"
    - "universal adversarial triggers for attacking and analyzing nlp"

Connected through hyperedge HE-5 (induced by source paper "how can we know what language models know"):
  with members:
    - "assessing bert s syntactic abilities"
    - "bert rediscovers the classical nlp pipeline"
    - "bert is not a knowledge base yet factual knowledge vs name based reasoning in unsupervised qa"
    - "barack s wife hillary using knowledge graphs for fact aware language modeling"
    - "mask predict parallel decoding of conditional masked language models"
    - "matching the blanks distributional similarity for relation learning"
    - "latent relation language models"
    - "knowledge enhanced contextual word representations"

Connected through hyperedge HE-6 (induced by source paper "on the evaluation of machine translation systems trained with back translation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "tagged back translation"
    - "the effect of translationese in machine translation test sets"
    - "bertscore evaluating text generation with bert"
    - "text repair model for neural machine translation"
    - "translationese in machine translation evaluation"
    - "attaining the unattainable reassessing claims of human parity in neural machine translation"
    - "investigating backtranslation in neural machine translation"

Connected through hyperedge HE-7 (induced by source paper "simple and effective noisy channel modeling for neural machine translation"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "achieving human parity on automatic chinese to english news translation"
    - "scaling neural machine translation"
    - "microsoft s submission to the wmt2018 news translation task how i learned to stop worrying and love the data"
    - "a call for clarity in reporting bleu scores"
    - "adaptive input representations for neural language modeling"
    - "understanding back translation at scale"
    - "attention is all you need"

Connected through hyperedge HE-8 (induced by source paper "depth adaptive transformer"):
  with members:
    - "fairseq a fast extensible toolkit for sequence modeling"
    - "universal transformers"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "adaptive input representations for neural language modeling"
    - "skipnet learning dynamic routing in convolutional networks"
    - "adaptive neural networks for efficient inference"
    - "attention is all you need"
    - "branchynet fast inference via early exiting from deep neural networks"

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)