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: "improving unsupervised word by word translation with language model and denoising autoencoder"
  Abstract: Unsupervised learning of cross-lingual word embedding offers elegant matching of words across languages, but has fundamental limitations in translating sentences. In this paper, we propose simple yet effective methods to improve word-by-word translation of cross-lingual embeddings, using only monolingual corpora but without any back-translation. We integrate a language model for context-aware search, and use a novel denoising autoencoder to handle reordering. Our system surpasses state-of-the-art unsupervised neural translation systems without costly iterative training. We also analyze the effect of vocabulary size and denoising type on the translation performance, which provides better understanding of learning the cross-lingual word embedding and its usage in translation.

Connected through hyperedge HE-1 (induced by source paper "denoising based sequence to sequence pre training for text generation"):
  with members:
    - "pre trained language model representations for language generation"
    - "improving grammatical error correction via pre training a copy augmented architecture with unlabeled data"
    - "mass masked sequence to sequence pre training for language generation"
    - "cross lingual language model pretraining"
    - "unified language model pre training for natural language understanding and generation"
    - "approaching neural grammatical error correction as a low resource machine translation task"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "sql to text generation with graph to sequence model"

Connected through hyperedge HE-2 (induced by source paper "effective cross lingual transfer of neural machine translation models without shared vocabularies"):
  with members:
    - "two new evaluation datasets for low resource machine translation nepali english and sinhala english"
    - "cross lingual language model pretraining"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "unsupervised statistical machine translation"
    - "loss in translation learning bilingual word mapping with a retrieval criterion"
    - "trivial transfer learning for low resource neural machine translation"
    - "universal neural machine translation for extremely low resource languages"
    - "parameter sharing methods for multilingual self attentional translation models"

Connected through hyperedge HE-3 (induced by source paper "do we really need fully unsupervised cross lingual embeddings"):
  with members:
    - "on the robustness of unsupervised and semi supervised cross lingual word embedding learning"
    - "a survey of cross lingual word embedding models"
    - "analyzing the limitations of cross lingual word embedding mappings"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "gromov wasserstein alignment of word embedding spaces"
    - "unsupervised multilingual word embeddings"
    - "generalizing procrustes analysis for better bilingual dictionary induction"
    - "a resource light method for cross lingual semantic textual similarity"

Connected through hyperedge HE-4 (induced by source paper "zero shot paraphrase generation with multilingual language models"):
  with members:
    - "improved zero shot neural machine translation via ignoring spurious correlations"
    - "mass masked sequence to sequence pre training for language generation"
    - "decomposable neural paraphrase generation"
    - "cross lingual language model pretraining"
    - "unified language model pre training for natural language understanding and generation"
    - "the missing ingredient in zero shot neural machine translation"
    - "generating wikipedia by summarizing long sequences"
    - "unsupervised natural language generation with denoising autoencoders"

Connected through hyperedge HE-5 (induced by source paper "translating translationese a two step approach to unsupervised machine translation"):
  with members:
    - "achieving human parity on automatic chinese to english news translation"
    - "unsupervised neural machine translation with weight sharing"
    - "a teacher student framework for zero resource neural machine translation"
    - "attention is all you need"
    - "effective strategies in zero shot neural machine translation"
    - "unsupervised neural machine translation"
    - "six challenges for neural machine translation"
    - "zero resource translation with multi lingual neural machine translation"

Connected through hyperedge HE-6 (induced by source paper "ordering matters word ordering aware unsupervised nmt"):
  with members:
    - "an effective approach to unsupervised machine translation"
    - "extract and edit an alternative to back translation for unsupervised neural machine translation"
    - "unsupervised statistical machine translation"
    - "addressing word order divergence in multilingual neural machine translation for extremely low resource languages"
    - "on difficulties of cross lingual transfer with order differences a case study on dependency parsing"
    - "unsupervised neural machine translation with weight sharing"
    - "mtnt a testbed for machine translation of noisy text"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "gromov wasserstein alignment of word embedding spaces"
    - "unsupervised multilingual word embeddings"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "phrase based neural unsupervised machine translation"
    - "a broad coverage challenge corpus for sentence understanding through inference"
    - "cross lingual models of word embeddings an empirical comparison"

Connected through hyperedge HE-8 (induced by source paper "towards unsupervised speech to text translation"):
  with members:
    - "augmenting librispeech with french translations a multimodal corpus for direct speech translation evaluation"
    - "unsupervised cross modal alignment of speech and text embedding spaces"
    - "unsupervised statistical machine translation"
    - "end to end automatic speech translation of audiobooks"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "speech2vec a sequence to sequence framework for learning word embeddings from speech"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"
    - "phrase based neural unsupervised 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)