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: "gated hierarchical attention for image captioning"
  Abstract: Attention modules connecting encoder and decoders have been widely applied in the field of object recognition, image captioning, visual question answering and neural machine translation, and significantly improves the performance. In this paper, we propose a bottom-up gated hierarchical attention (GHA) mechanism for image captioning. Our proposed model employs a CNN as the decoder which is able to learn different concepts at different layers, and apparently, different concepts correspond to different areas of an image. Therefore, we develop the GHA in which low-level concepts are merged into high-level concepts and simultaneously low-level attended features pass to the top to make predictions. Our GHA significantly improves the performance of the model that only applies one level attention, for example, the CIDEr score increases from 0.923 to 0.999, which is comparable to the state-of-the-art models that employ attributes boosting and reinforcement learning (RL). We also conduct extensive experiments to analyze the CNN decoder and our proposed GHA, and we find that deeper decoders cannot obtain better performance, and when the convolutional decoder becomes deeper the model is likely to collapse during training.

Connected through hyperedge HE-1 (induced by source paper "describing like humans on diversity in image captioning"):
  with members:
    - "cnn cnn convolutional decoders for image captioning"
    - "discriminability objective for training descriptive captions"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "learning to evaluate image captioning"
    - "improved image captioning via policy gradient optimization of spider"
    - "speaking the same language matching machine to human captions by adversarial training"
    - "convolutional image captioning"
    - "diverse and accurate image description using a variational auto encoder with an additive gaussian encoding space"

Connected through hyperedge HE-2 (induced by source paper "stack vs stacked visual semantic attention for image caption generation"):
  with members:
    - "learning to guide decoding for image captioning"
    - "mat a multimodal attentive translator for image captioning"
    - "stack captioning coarse to fine learning for image captioning"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "image captioning with semantic attention"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "boosting image captioning with attributes"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"

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.CV(Computer Vision and Pattern Recognition)