human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "transfer learning by ranking for weakly supervised object annotation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "m adda unsupervised domain adaptation with deep metric learning")
  Member "beyond pixels a comprehensive survey from bottom up to semantic image segmentation and cosegmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "waterfall atrous spatial pooling architecture for efficient semantic segmentation")
  Member "salient object detection a benchmark":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hallucinating statistical moment and subspace descriptors from object and saliency detectors for action recognition")
  Member "unsupervised object discovery and localization in the wild part based matching with bottom up region proposals":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "object discovery with a copy pasting gan")
  Member "weakly supervised discovery of visual pattern configurations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "training object detectors from few weakly labeled and many unlabeled images")
  Member "imagenet large scale visual recognition challenge":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "category specific object reconstruction from a single image":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reactive navigation in partially familiar planar environments using semantic perceptual feedback")

Question: Please tell me which class the source paper 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)