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: "a visual attention grounding neural model for multimodal machine translation"
  Abstract: We introduce a novel multimodal machine translation model that utilizes parallel visual and textual information. Our model jointly optimizes the learning of a shared visual-language embedding and a translator. The model leverages a visual attention grounding mechanism that links the visual semantics with the corresponding textual semantics. Our approach achieves competitive state-of-the-art results on the Multi30K and the Ambiguous COCO datasets. We also collected a new multilingual multimodal product description dataset to simulate a real-world international online shopping scenario. On this dataset, our visual attention grounding model outperforms other methods by a large margin.

Connected through hyperedge HE-1 (induced by source paper "a formal analysis of multimodal referring strategies under common ground"):
  with members:
    - "grounding language attributes to objects using bayesian eigenobjects"
    - "generating justifications for norm related agent decisions"
    - "multimodal attention branch network for perspective free sentence generation"
    - "multimodal continuation style architectures for human robot interaction"
    - "interactive visual grounding of referring expressions for human robot interaction"
    - "neuralreg an end to end approach to referring expression generation"
    - "multimodal grounding for language processing"
    - "the something something video database for learning and evaluating visual common sense"

Connected through hyperedge HE-2 (induced by source paper "visual agreement regularized training for multi modal machine translation"):
  with members:
    - "from words to sentences a progressive learning approach for zero resource machine translation with visual pivots"
    - "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"
    - "latent variable model for multi modal translation"
    - "phrase based neural unsupervised machine translation"
    - "imagination improves multimodal translation"

Connected through hyperedge HE-3 (induced by source paper "debiasing word embeddings improves multimodal machine translation"):
  with members:
    - "multimodal machine translation with embedding prediction"
    - "lium cvc submissions for wmt18 multimodal translation task"
    - "cuni system for the wmt18 multimodal translation task"
    - "when and why are pre trained word embeddings useful for neural machine translation"
    - "imagination improves multimodal translation"
    - "nmtpy a flexible toolkit for advanced neural machine translation systems"
    - "doubly attentive decoder for multi modal neural machine translation"
    - "all but the top simple and effective postprocessing for word representations"

Connected through hyperedge HE-4 (induced by source paper "from words to sentences a progressive learning approach for zero resource machine translation with visual pivots"):
  with members:
    - "unsupervised multi modal neural machine translation"
    - "zero resource neural machine translation with multi agent communication game"
    - "phrase based neural unsupervised machine translation"
    - "a teacher student framework for zero resource neural machine translation"
    - "unsupervised neural machine translation"
    - "zero resource machine translation by multimodal encoder decoder network with multimedia pivot"
    - "multi30k multilingual english german image descriptions"
    - "transfer learning for low resource neural machine translation"

Connected through hyperedge HE-5 (induced by source paper "hindi visual genome a dataset for multimodal english to hindi machine translation"):
  with members:
    - "imagination improves multimodal translation"
    - "image grounded conversations multimodal context for natural question and response generation"
    - "mat a multimodal attentive translator for image captioning"
    - "multi30k multilingual english german image descriptions"
    - "dense captioning with joint inference and visual context"
    - "flickr30k entities collecting region to phrase correspondences for richer image to sentence models"
    - "efficient estimation of word representations in vector space"

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)