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: "umons submission for wmt18 multimodal translation task"
  Abstract: This paper describes the UMONS solution for the Multimodal Machine Translation Task presented at the third conference on machine translation (WMT18). We explore a novel architecture, called deepGRU, based on recent findings in the related task of Neural Image Captioning (NIC). The models presented in the following sections lead to the best METEOR translation score for both constrained (English, image) -> German and (English, image) -> French sub-tasks.

Connected through hyperedge HE-1 (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"
    - "imagination improves multimodal translation"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "bringing back simplicity and lightliness into neural image captioning"):
  with members:
    - "improved image captioning via policy gradient optimization of spider"
    - "modulating and attending the source image during encoding improves multimodal translation"
    - "attention is all you need"
    - "visually grounded word embeddings and richer visual features for improving multimodal neural machine translation"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "an empirical study on the effectiveness of images in multimodal neural machine translation"
    - "lium cvc submissions for wmt17 multimodal translation task"
    - "review networks for caption generation"

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)