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: "an iterative co saliency framework for rgbd images"
  Abstract: As a newly emerging and significant topic in computer vision community, co-saliency detection aims at discovering the common salient objects in multiple related images. The existing methods often generate the co-saliency map through a direct forward pipeline which is based on the designed cues or initialization, but lack the refinement-cycle scheme. Moreover, they mainly focus on RGB image and ignore the depth information for RGBD images. In this paper, we propose an iterative RGBD co-saliency framework, which utilizes the existing single saliency maps as the initialization, and generates the final RGBD cosaliency map by using a refinement-cycle model. Three schemes are employed in the proposed RGBD co-saliency framework, which include the addition scheme, deletion scheme, and iteration scheme. The addition scheme is used to highlight the salient regions based on intra-image depth propagation and saliency propagation, while the deletion scheme filters the saliency regions and removes the non-common salient regions based on interimage constraint. The iteration scheme is proposed to obtain more homogeneous and consistent co-saliency map. Furthermore, a novel descriptor, named depth shape prior, is proposed in the addition scheme to introduce the depth information to enhance identification of co-salient objects. The proposed method can effectively exploit any existing 2D saliency model to work well in RGBD co-saliency scenarios. The experiments on two RGBD cosaliency datasets demonstrate the effectiveness of our proposed framework.

Connected through hyperedge HE-1 (induced by source paper "the devil is in the channels mutual channel loss for fine grained image classification"):
  with members:
    - "deep learning for free hand sketch a survey"
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multi graph transformer for free hand sketch recognition"
    - "looking for the devil in the details learning trilinear attention sampling network for fine grained image recognition"
    - "weakly supervised complementary parts models for fine grained image classification from the bottom up"
    - "object part attention model for fine grained image classification"
    - "cosface large margin cosine loss for deep face recognition"
    - "learning to navigate for fine grained classification"

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"
    - "review of visual saliency detection with comprehensive information"
    - "hscs hierarchical sparsity based co saliency detection for rgbd images"
    - "reverse attention for salient object detection"
    - "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 "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-4 (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"
    - "co saliency detection for rgbd images based on multi constraint feature matching and cross label propagation"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "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-5 (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"
    - "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-6 (induced by source paper "an underwater image enhancement benchmark dataset and beyond"):
  with members:
    - "diving deeper into underwater image enhancement a survey"
    - "salient object detection in the deep learning era an in depth survey"
    - "nested network with two stream pyramid for salient object detection in optical remote sensing images"
    - "watergan unsupervised generative network to enable real time color correction of monocular underwater images"
    - "emerging from water underwater image color correction based on weakly supervised color transfer"
    - "underwater single image color restoration using haze lines and a new quantitative dataset"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "photographic image synthesis with cascaded refinement networks"

Connected through hyperedge HE-7 (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"
    - "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-8 (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"
    - "structure measure a new way to evaluate foreground maps"
    - "group wise deep co saliency detection"

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)