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: "from words to sentences a progressive learning approach for zero resource machine translation with visual pivots"
  Abstract: The neural machine translation model has suffered from the lack of large-scale parallel corpora. In contrast, we humans can learn multi-lingual translations even without parallel texts by referring our languages to the external world. To mimic such human learning behavior, we employ images as pivots to enable zero-resource translation learning. However, a picture tells a thousand words, which makes multi-lingual sentences pivoted by the same image noisy as mutual translations and thus hinders the translation model learning. In this work, we propose a progressive learning approach for image-pivoted zero-resource machine translation. Since words are less diverse when grounded in the image, we first learn word-level translation with image pivots, and then progress to learn the sentence-level translation by utilizing the learned word translation to suppress noises in image-pivoted multi-lingual sentences. Experimental results on two widely used image-pivot translation datasets, IAPR-TC12 and Multi30k, show that the proposed approach significantly outperforms other state-of-the-art methods.

Connected through hyperedge HE-1 (induced by source paper "unpaired cross lingual image caption generation with self supervised rewards"):
  with members:
    - "unsupervised bilingual lexicon induction from mono lingual multimodal data"
    - "good news everyone context driven entity aware captioning for news images"
    - "discriminability objective for training descriptive captions"
    - "context aware visual policy network for sequence level image captioning"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "unsupervised image captioning"
    - "unpaired image captioning by language pivoting"
    - "using artificial tokens to control languages for multilingual image caption generation"

Connected through hyperedge HE-2 (induced by source paper "visual agreement regularized training for multi modal machine translation"):
  with members:
    - "distilling translations with visual awareness"
    - "unsupervised multi modal neural machine translation"
    - "regularizing neural machine translation by target bidirectional agreement"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "a visual attention grounding neural model for multimodal machine translation"
    - "latent variable model for multi modal translation"
    - "phrase based neural unsupervised machine translation"
    - "imagination improves multimodal 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)