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: "group wise deep co saliency detection"
  Abstract: In this paper, we propose an end-to-end group-wise deep co-saliency detection approach to address the co-salient object discovery problem based on the fully convolutional network (FCN) with group input and group output. The proposed approach captures the group-wise interaction information for group images by learning a semantics-aware image representation based on a convolutional neural network, which adaptively learns the group-wise features for co-saliency detection. Furthermore, the proposed approach discovers the collaborative and interactive relationships between group-wise feature representation and single-image individual feature representation, and model this in a collaborative learning framework. Finally, we set up a unified end-to-end deep learning scheme to jointly optimize the process of group-wise feature representation learning and the collaborative learning, leading to more reliable and robust co-saliency detection results. Experimental results demonstrate the effectiveness of our approach in comparison with the state-of-the-art approaches.

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"
    - "review of visual saliency detection with comprehensive information"
    - "iterative visual reasoning beyond convolutions"

Connected through hyperedge HE-2 (induced by source paper "deep object co segmentation via spatial semantic network modulation"):
  with members:
    - "deep high resolution representation learning for human pose estimation"
    - "gnn film graph neural networks with feature wise linear modulation"
    - "saliency for fine grained object recognition in domains with scarce training data"
    - "show match and segment joint learning of semantic matching and object co segmentation"
    - "semantic aware attention based deep object co segmentation"
    - "cross modulation networks for few shot learning"
    - "efficient video object segmentation via network modulation"
    - "deep object co segmentation"

Connected through hyperedge HE-3 (induced by source paper "an end to end network for co saliency detection in one single image"):
  with members:
    - "pyramid feature attention network for saliency detection"
    - "video salient object detection via fully convolutional networks"
    - "picanet learning pixel wise contextual attention for saliency detection"
    - "mask r cnn"
    - "deep contrast learning for salient object detection"
    - "training region based object detectors with online hard example mining"
    - "visual saliency based on multiscale deep features"
    - "u net convolutional networks for biomedical image segmentation"

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"
    - "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"
    - "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"

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)