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: "unsupervised statistical machine translation"
  Abstract: While modern machine translation has relied on large parallel corpora, a recent line of work has managed to train Neural Machine Translation (NMT) systems from monolingual corpora only (Artetxe et al., 2018c; Lample et al., 2018). Despite the potential of this approach for low-resource settings, existing systems are far behind their supervised counterparts, limiting their practical interest. In this paper, we propose an alternative approach based on phrase-based Statistical Machine Translation (SMT) that significantly closes the gap with supervised systems. Our method profits from the modular architecture of SMT: we first induce a phrase table from monolingual corpora through cross-lingual embedding mappings, combine it with an n-gram language model, and fine-tune hyperparameters through an unsupervised MERT variant. In addition, iterative backtranslation improves results further, yielding, for instance, 14.08 and 26.22 BLEU points in WMT 2014 English-German and English-French, respectively, an improvement of more than 7-10 BLEU points over previous unsupervised systems, and closing the gap with supervised SMT (Moses trained on Europarl) down to 2-5 BLEU points. Our implementation is available at this https URL

Connected through hyperedge HE-1 (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"
    - "an effective approach to unsupervised machine translation"
    - "mass masked sequence to sequence pre training for language generation"
    - "investigating backtranslation in neural 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-2 (induced by source paper "mass masked sequence to sequence pre training for language generation"):
  with members:
    - "unsupervised pivot translation for distant languages"
    - "multilingual neural machine translation with knowledge distillation"
    - "cross lingual language model pretraining"
    - "achieving human parity on automatic chinese to english news translation"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "double path networks for sequence to sequence learning"
    - "an efficient framework for learning sentence representations"

Connected through hyperedge HE-3 (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"
    - "improving unsupervised word by word translation with language model and denoising autoencoder"
    - "cross lingual language model pretraining"
    - "multilingual neural machine translation with soft decoupled encoding"
    - "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-4 (induced by source paper "revisiting low resource neural machine translation a case study"):
  with members:
    - "pay less attention with lightweight and dynamic convolutions"
    - "revisiting character based neural machine translation with capacity and compression"
    - "trivial transfer learning for low resource neural machine translation"
    - "pervasive attention 2d convolutional neural networks for sequence to sequence prediction"
    - "universal neural machine translation for extremely low resource languages"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "a call for clarity in reporting bleu scores"
    - "rapid adaptation of neural machine translation to new languages"

Connected through hyperedge HE-5 (induced by source paper "embedding projection for targeted cross lingual sentiment model comparisons and a real world study"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "bilingual sentiment embeddings joint projection of sentiment across languages"
    - "multibooked a corpus of basque and catalan hotel reviews annotated for aspect level sentiment classification"
    - "recurrent entity networks with delayed memory update for targeted aspect based sentiment analysis"
    - "aspect based sentiment analysis with gated convolutional networks"
    - "are all languages equally hard to language model"
    - "unsupervised machine translation using monolingual corpora only"
    - "attention is all you need"

Connected through hyperedge HE-6 (induced by source paper "unsupervised question answering by cloze translation"):
  with members:
    - "evaluating rewards for question generation models"
    - "cross lingual language model pretraining"
    - "multiple attribute text style transfer"
    - "efficient and robust question answering from minimal context over documents"
    - "knowledgeable reader enhancing cloze style reading comprehension with external commonsense knowledge"
    - "dynamic integration of background knowledge in neural nlu systems"
    - "what makes reading comprehension questions easier"
    - "identifying well formed natural language questions"

Connected through hyperedge HE-7 (induced by source paper "evaluating word embedding models methods and experimental results"):
  with members:
    - "cross lingual language model pretraining"
    - "semantic structure and interpretability of word embeddings"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a survey of word embeddings evaluation methods"
    - "post processing of word representations via variance normalization and dynamic embedding"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"

Connected through hyperedge HE-8 (induced by source paper "towards interlingua neural machine translation"):
  with members:
    - "the missing ingredient in zero shot neural machine translation"
    - "zero shot cross lingual classification using multilingual neural machine translation"
    - "xnli evaluating cross lingual sentence representations"
    - "fast decoding in sequence models using discrete latent variables"
    - "a neural interlingua for multilingual machine translation"
    - "phrase based neural unsupervised machine translation"
    - "an empirical analysis of nmt derived interlingual embeddings and their use in parallel sentence identification"
    - "unsupervised machine translation using monolingual corpora only"

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)