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: "gazedpm early integration of gaze information in deformable part models"
  Abstract: An increasing number of works explore collaborative human-computer systems in which human gaze is used to enhance computer vision systems. For object detection these efforts were so far restricted to late integration approaches that have inherent limitations, such as increased precision without increase in recall. We propose an early integration approach in a deformable part model, which constitutes a joint formulation over gaze and visual data. We show that our GazeDPM method improves over the state-of-the-art DPM baseline by 4% and a recent method for gaze-supported object detection by 3% on the public POET dataset. Our approach additionally provides introspection of the learnt models, can reveal salient image structures, and allows us to investigate the interplay between gaze attracting and repelling areas, the importance of view-specific models, as well as viewers' personal biases in gaze patterns. We finally study important practical aspects of our approach, such as the impact of using saliency maps instead of real fixations, the impact of the number of fixations, as well as robustness to gaze estimation error.

Connected through hyperedge HE-1 (induced by source paper "a self validation network for object level human attention estimation"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "scaling egocentric vision the epic kitchens dataset"
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "object level visual reasoning in videos"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "attention is all you need"
    - "the kinetics human action video dataset"
    - "instance level salient object segmentation"

Connected through hyperedge HE-2 (induced by source paper "seeing with humans gaze assisted neural image captioning"):
  with members:
    - "image captioning with semantic attention"
    - "language models for image captioning the quirks and what works"
    - "show attend and tell neural image caption generation with visual attention"
    - "appearance based gaze estimation in the wild"
    - "learning like a child fast novel visual concept learning from sentence descriptions of images"
    - "microsoft coco captions data collection and evaluation server"
    - "draw a recurrent neural network for image generation"
    - "places205 vggnet models for scene recognition"

Connected through hyperedge HE-3 (induced by source paper "gaze embeddings for zero shot image classification"):
  with members:
    - "seeing with humans gaze assisted neural image captioning"
    - "label embedding for image classification"
    - "latent embeddings for zero shot classification"
    - "evaluation of output embeddings for fine grained image classification"
    - "prediction of search targets from fixations in open world settings"
    - "going deeper with convolutions"
    - "multiple instance reinforcement learning for efficient weakly supervised detection in images"
    - "the secrets of salient object segmentation"

Connected through hyperedge HE-4 (induced by source paper "predicting the category and attributes of visual search targets using deep gaze pooling"):
  with members:
    - "seeing with humans gaze assisted neural image captioning"
    - "gaze embeddings for zero shot image classification"
    - "whittlesearch interactive image search with relative attribute feedback"
    - "learning deep features for discriminative localization"
    - "prediction of search targets from fixations in open world settings"
    - "multiple instance reinforcement learning for efficient weakly supervised detection in images"
    - "the secrets of salient object segmentation"
    - "deep inside convolutional networks visualising image classification models and saliency maps"

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)