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: "a review of co saliency detection technique fundamentals applications and challenges"
  Abstract: Co-saliency detection is a newly emerging and rapidly growing research area in computer vision community. As a novel branch of visual saliency, co-saliency detection refers to the discovery of common and salient foregrounds from two or more relevant images, and can be widely used in many computer vision tasks. The existing co-saliency detection algorithms mainly consist of three components: extracting effective features to represent the image regions, exploring the informative cues or factors to characterize co-saliency, and designing effective computational frameworks to formulate co-saliency. Although numerous methods have been developed, the literature is still lacking a deep review and evaluation of co-saliency detection techniques. In this paper, we aim at providing a comprehensive review of the fundamentals, challenges, and applications of co-saliency detection. Specifically, we provide an overview of some related computer vision works, review the history of co-saliency detection, summarize and categorize the major algorithms in this research area, discuss some open issues in this area, present the potential applications of co-saliency detection, and finally point out some unsolved challenges and promising future works. We expect this review to be beneficial to both fresh and senior researchers in this field, and give insights to researchers in other related areas regarding the utility of co-saliency detection algorithms.

Connected through hyperedge HE-1 (induced by source paper "co salient object detection based on deep saliency networks and seed propagation over an integrated graph"):
  with members:
    - "end to end saliency mapping via probability distribution prediction"
    - "deep saliency with encoded low level distance map and high level features"
    - "deep contrast learning for salient object detection"
    - "deep image retrieval learning global representations for image search"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "shallow and deep convolutional networks for saliency prediction"
    - "recurrent attentional networks for saliency detection"
    - "visual saliency based on multiscale deep features"

Connected through hyperedge HE-2 (induced by source paper "context proposals for saliency detection"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "deep contrast learning for salient object detection"
    - "fastmask segment multi scale object candidates in one shot"
    - "learning to refine object segments"
    - "what makes for effective detection proposals"
    - "shallow and deep convolutional networks for saliency prediction"
    - "deepsaliency multi task deep neural network model for salient object detection"
    - "visual saliency based on multiscale deep features"

Connected through hyperedge HE-3 (induced by source paper "an iterative co saliency framework for rgbd images"):
  with members:
    - "co saliency detection for rgbd images based on multi constraint feature matching and cross label propagation"
    - "saliency detection for stereoscopic images based on depth confidence analysis and multiple cues fusion"
    - "disc deep image saliency computing via progressive representation learning"
    - "deep saliency with encoded low level distance map and high level features"
    - "deep contrast learning for salient object detection"
    - "inner and inter label propagation salient object detection in the wild"

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)