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: "visually grounded word embeddings and richer visual features for improving multimodal neural machine translation"
  Abstract: In Multimodal Neural Machine Translation (MNMT), a neural model generates a translated sentence that describes an image, given the image itself and one source descriptions in English. This is considered as the multimodal image caption translation task. The images are processed with Convolutional Neural Network (CNN) to extract visual features exploitable by the translation model. So far, the CNNs used are pre-trained on object detection and localization task. We hypothesize that richer architecture, such as dense captioning models, may be more suitable for MNMT and could lead to improved translations. We extend this intuition to the word-embeddings, where we compute both linguistic and visual representation for our corpus vocabulary. We combine and compare different confi

Connected through hyperedge HE-1 (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"
    - "modulating and attending the source image during encoding improves multimodal translation"
    - "attention is all you need"
    - "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-2 (induced by source paper "a knowledge grounded neural conversation model"):
  with members:
    - "learning symmetric collaborative dialogue agents with dynamic knowledge graph embeddings"
    - "a persona based neural conversation model"
    - "how not to evaluate your dialogue system an empirical study of unsupervised evaluation metrics for dialogue response generation"
    - "multi domain neural network language generation for spoken dialogue systems"
    - "learning end to end goal oriented dialog"
    - "dialog state tracking a machine reading approach using memory network"
    - "end to end memory networks"
    - "deltableu a discriminative metric for generation tasks with intrinsically diverse targets"

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)