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 "review of visual saliency detection with comprehensive information"):
  Abstract: Visual saliency detection model simulates the human visual system to perceive the scene, and has been widely used in many vision tasks. With the acquisition technology development, more comprehensive information, such as depth cue, inter-image correspondence, or temporal relationship, is available to extend image saliency detection to RGBD saliency detection, co-saliency detection, or video saliency detection. RGBD saliency detection model focuses on extracting the salient regions from RGBD images by combining the depth information. Co-saliency detection model introduces the inter-image correspondence constraint to discover the common salient object in an image group. The goal of video saliency detection model is to locate the motion-related salient object in video sequences, which considers the motion cue and spatiotemporal constraint jointly. In this paper, we review different types of saliency detection algorithms, summarize the important issues of the existing methods, and discuss the existent problems and future works. Moreover, the evaluation datasets and quantitative measurements are briefly introduced, and the experimental analysis and discission are conducted to provide a holistic overview of different saliency detection methods.

Includes members:
  Member "deeply supervised salient object detection with short connections":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "co saliency detection for rgbd images based on multi constraint feature matching and cross label propagation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "rethinking rgb d salient object detection models datasets and large scale benchmarks")
  Member "video salient object detection via fully convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning video object segmentation from unlabeled videos")
  Member "learning uncertain convolutional features for accurate saliency detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "rgbd salient object detection via deep fusion":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "depth potentiality aware gated attention network for rgb d salient object detection")
  Member "an iterative co saliency framework for rgbd images":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the devil is in the channels mutual channel loss for fine grained image classification")
  Member "integrated deep and shallow networks for salient object detection":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep unsupervised saliency detection a multiple noisy labeling perspective")
  Member "group wise deep co saliency detection":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adaptive graph convolutional network with attention graph clustering for co saliency detection")

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)