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: "modulating and attending the source image during encoding improves multimodal translation"
  Abstract: We propose a new and fully end-to-end approach for multimodal translation where the source text encoder modulates the entire visual input processing using conditional batch normalization, in order to compute the most informative image features for our task. Additionally, we propose a new attention mechanism derived from this original idea, where the attention model for the visual input is conditioned on the source text encoder representations. In the paper, we detail our models as well as the image analysis pipeline. Finally, we report experimental results. They are, as far as we know, the new state of the art on three different test sets.

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"
    - "umons submission for wmt18 multimodal translation task"
    - "imagination 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 "visual reasoning with multi hop feature modulation"):
  with members:
    - "efficient video object segmentation via network modulation"
    - "mattnet modular attention network for referring expression comprehension"
    - "guide me interacting with deep networks"
    - "modulating early visual processing by language"
    - "visual question answering datasets algorithms and future challenges"
    - "comprehension guided referring expressions"
    - "parallel attention a unified framework for visual object discovery through dialogs and queries"
    - "film visual reasoning with a general conditioning layer"

Connected through hyperedge HE-3 (induced by source paper "bringing back simplicity and lightliness into neural image captioning"):
  with members:
    - "umons submission for wmt18 multimodal translation task"
    - "improved image captioning via policy gradient optimization of spider"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "umons submission for wmt18 multimodal translation task"):
  with members:
    - "doubly attentive decoder for multi modal neural machine translation"
    - "tips and tricks for visual question answering learnings from the 2017 challenge"
    - "an empirical study on the effectiveness of images in multimodal neural machine translation"
    - "does multimodality help human and machine for translation and image captioning"
    - "neural machine translation of rare words with subword units"
    - "adam a method for stochastic optimization"
    - "neural machine translation by jointly learning to align and translate"

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)