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: "an effective approach to unsupervised machine translation"
  Abstract: While machine translation has traditionally relied on large amounts of parallel corpora, a recent research line has managed to train both Neural Machine Translation (NMT) and Statistical Machine Translation (SMT) systems using monolingual corpora only. In this paper, we identify and address several deficiencies of existing unsupervised SMT approaches by exploiting subword information, developing a theoretically well founded unsupervised tuning method, and incorporating a joint refinement procedure. Moreover, we use our improved SMT system to initialize a dual NMT model, which is further fine-tuned through on-the-fly back-translation. Together, we obtain large improvements over the previous state-of-the-art in unsupervised machine translation. For instance, we get 22.5 BLEU points in English-to-German WMT 2014, 5.5 points more than the previous best unsupervised system, and 0.5 points more than the (supervised) shared task winner back in 2014.

Connected through hyperedge HE-1 (induced by source paper "a multilingual view of unsupervised machine translation"):
  with members:
    - "a probabilistic formulation of unsupervised text style transfer"
    - "multilingual denoising pre training for neural machine translation"
    - "improved zero shot neural machine translation via ignoring spurious correlations"
    - "massively multilingual neural machine translation in the wild findings and challenges"
    - "explicit cross lingual pre training for unsupervised machine translation"
    - "mass masked sequence to sequence pre training for language generation"
    - "consistency by agreement in zero shot neural machine translation"
    - "cross lingual language model pretraining"

Connected through hyperedge HE-2 (induced by source paper "a probabilistic formulation of unsupervised text style transfer"):
  with members:
    - "evaluating style transfer for text"
    - "unsupervised text style transfer using language models as discriminators"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "dear sir or madam may i introduce the yafc corpus corpus benchmarks and metrics for formality style transfer"
    - "phrase based neural unsupervised machine translation"
    - "toward controlled generation of text"
    - "style transfer from non parallel text by cross alignment"

Connected through hyperedge HE-3 (induced by source paper "do all roads lead to rome understanding the role of initialization in iterative back translation"):
  with members:
    - "multilingual denoising pre training for neural machine translation"
    - "using monolingual data in neural machine translation a systematic study"
    - "mass masked sequence to sequence pre training for language generation"
    - "investigating backtranslation in neural machine translation"
    - "unsupervised statistical machine translation"
    - "unsupervised neural machine translation initialized by unsupervised statistical machine translation"
    - "scaling neural machine translation"
    - "phrase based neural unsupervised machine translation"

Connected through hyperedge HE-4 (induced by source paper "putting machine translation in context with the noisy channel model"):
  with members:
    - "facebook fair s wmt19 news translation task submission"
    - "simple and effective noisy channel modeling for neural machine translation"
    - "pre trained language model representations for language generation"
    - "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-5 (induced by source paper "ordering matters word ordering aware unsupervised nmt"):
  with members:
    - "extract and edit an alternative to back translation for unsupervised neural machine translation"
    - "improving unsupervised word by word translation with language model and denoising autoencoder"
    - "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-6 (induced by source paper "bilingual lexicon induction through unsupervised machine translation"):
  with members:
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "gromov wasserstein alignment of word embedding spaces"
    - "unsupervised statistical machine translation"
    - "unsupervised cross lingual information retrieval using monolingual data only"
    - "non adversarial unsupervised word translation"
    - "unsupervised alignment of embeddings with wasserstein procrustes"
    - "learning unsupervised word mapping by maximizing mean discrepancy"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

Connected through hyperedge HE-7 (induced by source paper "lingua custodia at wmt 19 attempts to control terminology"):
  with members:
    - "a survey of domain adaptation for neural machine translation"
    - "fast lexically constrained decoding with dynamic beam allocation for neural machine translation"
    - "a call for clarity in reporting bleu scores"
    - "sockeye a toolkit for neural machine translation"
    - "attention is all you need"
    - "lexically constrained decoding for sequence generation using grid beam search"
    - "word translation without parallel data"
    - "improving neural machine translation models with monolingual data"

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)