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: "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.

Connected through hyperedge HE-1 (induced by source paper "adaptive graph convolutional network with attention graph clustering for co saliency detection"):
  with members:
    - "scene graph generation with external knowledge and image reconstruction"
    - "learning graph embedding with adversarial training methods"
    - "attributed graph clustering a deep attentional embedding approach"
    - "label efficient semi supervised learning via graph filtering"
    - "a comprehensive survey on graph neural networks"
    - "graph neural networks a review of methods and applications"
    - "iterative visual reasoning beyond convolutions"
    - "adaptive graph convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "global context aware progressive aggregation network for salient object detection"):
  with members:
    - "a simple pooling based design for real time salient object detection"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "deeply supervised salient object detection with short connections"
    - "hscs hierarchical sparsity based co saliency detection for rgbd images"
    - "reverse attention for salient object detection"
    - "an iterative co saliency framework for rgbd images"
    - "structure measure a new way to evaluate foreground maps"
    - "picanet learning pixel wise contextual attention for saliency detection"

Connected through hyperedge HE-3 (induced by source paper "dna deeply supervised nonlinear aggregation for salient object detection"):
  with members:
    - "pyramid feature attention network for saliency detection"
    - "quadruplet network with one shot learning for fast visual object tracking"
    - "a simple pooling based design for real time salient object detection"
    - "richer and deeper supervision network for salient object detection"
    - "cascaded partial decoder for fast and accurate salient object detection"
    - "deeply supervised salient object detection with short connections"
    - "deep reasoning with multi scale context for salient object detection"
    - "deep visual attention prediction"

Connected through hyperedge HE-4 (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-5 (induced by source paper "xrai better attributions through regions"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "neural network attributions a causal perspective"
    - "a note about local explanation methods for deep neural networks lack sensitivity to parameter values"
    - "what made you do this understanding black box decisions with sufficient input subsets"
    - "sanity checks for saliency maps"
    - "explaining nonlinear classification decisions with deep taylor decomposition"
    - "axiomatic attribution for deep networks"
    - "learning important features through propagating activation differences"

Connected through hyperedge HE-6 (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"
    - "co saliency detection for rgbd images based on multi constraint feature matching and cross label propagation"
    - "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-7 (induced by source paper "center emphasized visual saliency and a contrast based full reference image quality index"):
  with members:
    - "contrast and visual saliency similarity induced index for assessing image quality"
    - "gradient magnitude similarity deviation a highly efficient perceptual image quality index"

Connected through hyperedge HE-8 (induced by source paper "salient object detection in video using deep non local neural networks"):
  with members:
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "deep visual attention prediction"
    - "video salient object detection using spatiotemporal deep features"
    - "deepvs a deep learning based video saliency prediction approach"
    - "difnet semantic segmentation by diffusion networks"
    - "non local recurrent network for image restoration"
    - "video salient object detection via fully convolutional networks"

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)