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 neural machine translation with weight sharing"
  Abstract: Unsupervised neural machine translation (NMT) is a recently proposed approach for machine translation which aims to train the model without using any labeled data. The models proposed for unsupervised NMT often use only one shared encoder to map the pairs of sentences from different languages to a shared-latent space, which is weak in keeping the unique and internal characteristics of each language, such as the style, terminology, and sentence structure. To address this issue, we introduce an extension by utilizing two independent encoders but sharing some partial weights which are responsible for extracting high-level representations of the input sentences. Besides, two different generative adversarial networks (GANs), namely the local GAN and global GAN, are proposed to enhance the cross-language translation. With this new approach, we achieve significant improvements on English-German, English-French and Chinese-to-English translation tasks.

Connected through hyperedge HE-1 (induced by source paper "robust unsupervised neural machine translation with adversarial training"):
  with members:
    - "mass masked sequence to sequence pre training for language generation"
    - "extract and edit an alternative to back translation for unsupervised neural machine translation"
    - "training on synthetic noise improves robustness to natural noise in machine translation"
    - "cross lingual language model pretraining"
    - "robust neural machine translation with doubly adversarial inputs"
    - "towards robust neural machine translation"
    - "robust neural machine translation with joint textual and phonetic embedding"
    - "unsupervised machine translation using monolingual corpora only"

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"
    - "unsupervised statistical machine 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"

Connected through hyperedge HE-3 (induced by source paper "regressing word and sentence embeddings for regularization of neural machine translation"):
  with members:
    - "rewe regressing word embeddings for regularization of neural machine translation systems"
    - "pre trained language model representations for language generation"
    - "differentiable sampling with flexible reference word order for neural machine translation"
    - "regularizing neural machine translation by target bidirectional agreement"
    - "training neural machine translation using word embedding based loss"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "von mises fisher loss for training sequence to sequence models with continuous outputs"
    - "deep contextualized word representations"

Connected through hyperedge HE-4 (induced by source paper "extract and edit an alternative to back translation for unsupervised neural machine translation"):
  with members:
    - "towards robust neural machine translation"
    - "response generation by context aware prototype editing"
    - "delete retrieve generate a simple approach to sentiment and style transfer"
    - "extracting parallel sentences with bidirectional recurrent neural networks to improve machine translation"
    - "phrase based neural unsupervised machine translation"
    - "generating sentences by editing prototypes"
    - "a challenge set approach to evaluating machine translation"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "explicit cross lingual pre training for unsupervised machine translation"):
  with members:
    - "mass masked sequence to sequence pre training for language generation"
    - "unsupervised neural machine translation with smt as posterior regularization"
    - "cross lingual language model pretraining"
    - "unsupervised statistical machine translation"
    - "xnli evaluating cross lingual sentence representations"
    - "unsupervised neural machine translation initialized by unsupervised statistical machine translation"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint training for neural machine translation models with monolingual data"

Connected through hyperedge HE-6 (induced by source paper "unsupervised neural machine translation with smt as posterior regularization"):
  with members:
    - "on the impact of various types of noise on neural machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "regularizing neural machine translation by target bidirectional agreement"
    - "prior knowledge integration for neural machine translation using posterior regularization"
    - "joint training for neural machine translation models with monolingual data"
    - "phrase based neural unsupervised machine translation"
    - "unsupervised machine translation using monolingual corpora only"
    - "attention is all you need"

Connected through hyperedge HE-7 (induced by source paper "cross lingual training for automatic question generation"):
  with members:
    - "cross lingual language model pretraining"
    - "putting the horse before the cart a generator evaluator framework for question generation from text"
    - "automating reading comprehension by generating question and answer pairs"
    - "cross lingual transfer learning for multilingual task oriented dialog"
    - "phrase based neural unsupervised machine translation"
    - "learning to ask neural question generation for reading comprehension"
    - "attention is all you need"
    - "a joint model for question answering and question generation"

Connected through hyperedge HE-8 (induced by source paper "domain adaptation of neural machine translation by lexicon induction"):
  with members:
    - "a survey of domain adaptation for neural machine translation"
    - "phrase based neural unsupervised machine translation"
    - "data augmentation for low resource neural machine translation"
    - "attention is all you need"
    - "opennmt open source toolkit for neural machine translation"
    - "unsupervised neural machine translation"
    - "six challenges for neural machine translation"
    - "word translation without parallel 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)