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: "saliency detection for stereoscopic images based on depth confidence analysis and multiple cues fusion"
  Abstract: Stereoscopic perception is an important part of human visual system that allows the brain to perceive depth. However, depth information has not been well explored in existing saliency detection models. In this letter, a novel saliency detection method for stereoscopic images is proposed. Firstly, we propose a measure to evaluate the reliability of depth map, and use it to reduce the influence of poor depth map on saliency detection. Then, the input image is represented as a graph, and the depth information is introduced into graph construction. After that, a new definition of compactness using color and depth cues is put forward to compute the compactness saliency map. In order to compensate the detection errors of compactness saliency when the salient regions have similar appearances with background, foreground saliency map is calculated based on depth-refined foreground seeds selection mechanism and multiple cues contrast. Finally, these two saliency maps are integrated into a final saliency map through weighted-sum method according to their importance. Experiments on two publicly available stereo datasets demonstrate that the proposed method performs better than other 10 state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "rethinking rgb d salient object detection models datasets and large scale benchmarks"):
  with members:
    - "super diffusion for salient object detection"
    - "adaptive fusion for rgb d salient object detection"
    - "salient object detection in the deep learning era an in depth survey"
    - "salient object detection with lossless feature reflection and weighted structural loss"
    - "multi source weak supervision for saliency detection"
    - "saliency integration an arbitrator model"
    - "richer and deeper supervision network for salient object detection"
    - "deeply supervised salient object detection with short connections"

Connected through hyperedge HE-2 (induced by source paper "cmsalgan rgb d salient object detection with cross view generative adversarial networks"):
  with members:
    - "adaptive fusion for rgb d salient object detection"
    - "coupled generative adversarial network for continuous fine grained action segmentation"
    - "a simple pooling based design for real time salient object detection"
    - "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"
    - "sch gan semi supervised cross modal hashing by generative adversarial network"
    - "unsupervised cross modality domain adaptation of convnets for biomedical image segmentations with adversarial loss"
    - "self supervised adversarial hashing networks for cross modal retrieval"
    - "macro micro adversarial network for human parsing"

Connected through hyperedge HE-3 (induced by source paper "cnn based rgb d salient object detection learn select and fuse"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "co saliency detection for rgbd images based on multi constraint feature matching and cross label propagation"
    - "modality distillation with multiple stream networks for action recognition"
    - "rgbd salient object detection via deep fusion"
    - "an iterative co saliency framework for rgbd images"
    - "like what you like knowledge distill via neuron selectivity transfer"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "salient object detection a benchmark"

Connected through hyperedge HE-4 (induced by source paper "review of visual saliency detection with comprehensive information"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "co saliency detection for rgbd images based on multi constraint feature matching and cross label propagation"
    - "video salient object detection via fully convolutional networks"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "rgbd salient object detection via deep fusion"
    - "an iterative co saliency framework for rgbd images"
    - "integrated deep and shallow networks for salient object detection"
    - "group wise deep co saliency detection"

Connected through hyperedge HE-5 (induced by source paper "hscs hierarchical sparsity based co saliency detection for rgbd images"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "review of visual saliency detection with comprehensive information"
    - "co saliency detection for rgbd images based on multi constraint feature matching and cross label propagation"
    - "enhanced alignment measure for binary foreground map evaluation"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "rgbd salient object detection via deep fusion"
    - "an iterative co saliency framework for rgbd images"
    - "structure measure a new way to evaluate foreground maps"

Connected through hyperedge HE-6 (induced by source paper "co saliency detection for rgbd images based on multi constraint feature matching and cross label propagation"):
  with members:
    - "learning multilayer channel features for pedestrian detection"
    - "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"
    - "salient object detection a benchmark"
    - "very deep convolutional networks for large scale image recognition"
    - "learning rich features from rgb d images for object detection and segmentation"

Connected through hyperedge HE-7 (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"
    - "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"
    - "a review of co saliency detection technique fundamentals applications and challenges"
    - "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)