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: "imagination improves multimodal translation"
  Abstract: We decompose multimodal translation into two sub-tasks: learning to translate and learning visually grounded representations. In a multitask learning framework, translations are learned in an attention-based encoder-decoder, and grounded representations are learned through image representation prediction. Our approach improves translation performance compared to the state of the art on the Multi30K dataset. Furthermore, it is equally effective if we train the image prediction task on the external MS COCO dataset, and we find improvements if we train the translation model on the external News Commentary parallel text.

Connected through hyperedge HE-1 (induced by source paper "countering language drift via visual grounding"):
  with members:
    - "lessons learned in multilingual grounded language learning"
    - "translating neuralese"
    - "emergent communication in a multi modal multi step referential game"
    - "emergence of language with multi agent games learning to communicate with sequences of symbols"
    - "learning visually grounded sentence representations"
    - "natural language does not emerge naturally in multi agent dialog"
    - "towards neural machine translation with partially aligned corpora"
    - "vse improving visual semantic embeddings with hard negatives"

Connected through hyperedge HE-2 (induced by source paper "distilling translations with visual awareness"):
  with members:
    - "equalizing gender biases in neural machine translation with word embeddings techniques"
    - "probing the need for visual context in multimodal machine translation"
    - "achieving human parity on automatic chinese to english news translation"
    - "end to end image captioning exploits multimodal distributional similarity"
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "getting gender right in neural machine translation"
    - "object counts bringing explicit detections back into image captioning"
    - "cuni system for the wmt18 multimodal translation task"

Connected through hyperedge HE-3 (induced by source paper "multi head attention with diversity for learning grounded multilingual multimodal representations"):
  with members:
    - "lessons learned in multilingual grounded language learning"
    - "stacked cross attention for image text matching"
    - "learning visually grounded semantics from contrastive adversarial samples"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "multi head attention with disagreement regularization"
    - "look imagine and match improving textual visual cross modal retrieval with generative models"
    - "learning two branch neural networks for image text matching tasks"
    - "attention is all you need"

Connected through hyperedge HE-4 (induced by source paper "probing representations learned by multimodal recurrent and transformer models"):
  with members:
    - "input combination strategies for multi source transformer decoder"
    - "glue a multi task benchmark and analysis platform for natural language understanding"
    - "deep contextualized word representations"
    - "cuni system for the wmt18 multimodal translation task"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "interpretable textual neuron representations for nlp"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "cuni system for the wmt17 multimodal translation task"

Connected through hyperedge HE-5 (induced by source paper "bootstrapping disjoint datasets for multilingual multimodal representation learning"):
  with members:
    - "opennmt neural machine translation toolkit"
    - "lessons learned in multilingual grounded language learning"
    - "zero resource neural machine translation with multi agent communication game"
    - "learning visually grounded sentence representations"
    - "limitations of cross lingual learning from image search"
    - "vse improving visual semantic embeddings with hard negatives"
    - "emergent translation in multi agent communication"
    - "image pivoting for learning multilingual multimodal representations"

Connected through hyperedge HE-6 (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"
    - "a visual attention grounding neural model for multimodal machine translation"
    - "latent variable model for multi modal translation"
    - "phrase based neural unsupervised machine translation"

Connected through hyperedge HE-7 (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"
    - "a visual attention grounding neural model for multimodal machine 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-8 (induced by source paper "adversarial reconstruction for multi modal machine translation"):
  with members:
    - "lium cvc submissions for wmt18 multimodal translation task"
    - "unsupervised image captioning"
    - "spectral normalization for generative adversarial networks"
    - "umons submission for wmt18 multimodal translation task"
    - "modulating and attending the source image during encoding improves multimodal translation"
    - "multimodal compact bilinear pooling for multimodal neural machine translation"
    - "doubly attentive decoder for multi modal neural machine translation"
    - "improved training of wasserstein gans"

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)