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: "boosted attention leveraging human attention for image captioning"
  Abstract: Visual attention has shown usefulness in image captioning, with the goal of enabling a caption model to selectively focus on regions of interest. Existing models typically rely on top-down language information and learn attention implicitly by optimizing the captioning objectives. While somewhat effective, the learned top-down attention can fail to focus on correct regions of interest without direct supervision of attention. Inspired by the human visual system which is driven by not only the task-specific top-down signals but also the visual stimuli, we in this work propose to use both types of attention for image captioning. In particular, we highlight the complementary nature of the two types of attention and develop a model (Boosted Attention) to integrate them for image captioning. We validate the proposed approach with state-of-the-art performance across various evaluation metrics.

Connected through hyperedge HE-1 (induced by source paper "scene based factored attention for image captioning"):
  with members:
    - "learning to guide decoding for image captioning"
    - "discriminability objective for training descriptive captions"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "improved image captioning via policy gradient optimization of spider"
    - "show adapt and tell adversarial training of cross domain image captioner"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "bottom up and top down attention for image captioning and visual question answering"
    - "locally supervised deep hybrid model for scene recognition"

Connected through hyperedge HE-2 (induced by source paper "context and attribute grounded dense captioning"):
  with members:
    - "factual or emotional stylized image captioning with adaptive learning and attention"
    - "ts2c tight box mining with surrounding segmentation context for weakly supervised object detection"
    - "discriminability objective for training descriptive captions"
    - "modeling visual context is key to augmenting object detection datasets"
    - "zoom net mining deep feature interactions for visual relationship recognition"
    - "show tell and discriminate image captioning by self retrieval with partially labeled data"
    - "recurrent fusion network for image captioning"
    - "exploring visual relationship for image captioning"

Connected through hyperedge HE-3 (induced by source paper "manipulation skill assessment from videos with spatial attention network"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "mutual context network for jointly estimating egocentric gaze and actions"
    - "bam bottleneck attention module"
    - "learning to score the figure skating sports videos"
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "tool detection and operative skill assessment in surgical videos using region based convolutional neural networks"
    - "cbam convolutional block attention module"
    - "video and accelerometer based motion analysis for automated surgical skills assessment"

Connected through hyperedge HE-4 (induced by source paper "what i see is what you see joint attention learning for first and third person video co analysis"):
  with members:
    - "hierarchical recurrent neural network for video summarization"
    - "object part attention model for fine grained image classification"
    - "interaction aware spatio temporal pyramid attention networks for action classification"
    - "semantic aware attention based deep object co segmentation"
    - "factual or emotional stylized image captioning with adaptive learning and attention"
    - "cross view image synthesis using conditional gans"
    - "harmonious attention network for person re identification"
    - "global pose estimation with an attention based recurrent network"

Connected through hyperedge HE-5 (induced by source paper "human attention in image captioning dataset and analysis"):
  with members:
    - "understanding and visualizing deep visual saliency models"
    - "saliency prediction in the deep learning era an empirical investigation"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"
    - "sca cnn spatial and channel wise attention in convolutional networks for image captioning"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"

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)