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: "co saliency detection for rgbd images based on multi constraint feature matching and cross label propagation"
  Abstract: Co-saliency detection aims at extracting the common salient regions from an image group containing two or more relevant images. It is a newly emerging topic in computer vision community. Different from the most existing co-saliency methods focusing on RGB images, this paper proposes a novel co-saliency detection model for RGBD images, which utilizes the depth information to enhance identification of co-saliency. First, the intra saliency map for each image is generated by the single image saliency model, while the inter saliency map is calculated based on the multi-constraint feature matching, which represents the constraint relationship among multiple images. Then, the optimization scheme, namely cross label propagation, is used to refine the intra and inter saliency maps in a cross way. Finally, all the original and optimized saliency maps are integrated to generate the final co-saliency result. The proposed method introduces the depth information and multi-constraint feature matching to improve the performance of co-saliency detection. Moreover, the proposed method can effectively exploit any existing single image saliency model to work well in co-saliency scenarios. Experiments on two RGBD co-saliency datasets demonstrate the effectiveness of our proposed model.

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 "nested network with two stream pyramid for salient object detection in optical remote sensing images"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "review of visual saliency detection with comprehensive information"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "an iterative co saliency framework for rgbd images"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "structure measure a new way to evaluate foreground maps"
    - "remote sensing image scene classification benchmark and state of the art"
    - "deep contrast learning for salient object detection"

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"
    - "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"
    - "saliency detection for stereoscopic images based on depth confidence analysis and multiple cues fusion"
    - "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"
    - "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"
    - "saliency detection for stereoscopic images based on depth confidence analysis and multiple cues fusion"

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"
    - "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"
    - "group wise deep co saliency detection"

Connected through hyperedge HE-6 (induced by source paper "exploring to learn visual saliency the rl iac approach"):
  with members:
    - "an integration of bottom up and top down salient cues on rgb d data saliency from objectness versus non objectness"
    - "rgbd salient object detection via deep fusion"
    - "ssd single shot multibox detector"
    - "what makes for effective detection proposals"
    - "visual saliency detection based on multiscale deep cnn features"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "learning deep features for discriminative localization"

Connected through hyperedge HE-7 (induced by source paper "a cascaded convolutional neural network for single image dehazing"):
  with members:
    - "slow feature analysis for human action recognition"

Connected through hyperedge HE-8 (induced by source paper "an iterative co saliency framework for rgbd images"):
  with members:
    - "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"
    - "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)