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 via combining region level and pixel level predictions with cnns"
  Abstract: This paper proposes a novel saliency detection method by combining region-level saliency estimation and pixel-level saliency prediction with CNNs (denoted as CRPSD). For pixel-level saliency prediction, a fully convolutional neural network (called pixel-level CNN) is constructed by modifying the VGGNet architecture to perform multi-scale feature learning, based on which an image-to-image prediction is conducted to accomplish the pixel-level saliency detection. For region-level saliency estimation, an adaptive superpixel based region generation technique is first designed to partition an image into regions, based on which the region-level saliency is estimated by using a CNN model (called region-level CNN). The pixel-level and region-level saliencies are fused to form the final salient map by using another CNN (called fusion CNN). And the pixel-level CNN and fusion CNN are jointly learned. Extensive quantitative and qualitative experiments on four public benchmark datasets demonstrate that the proposed method greatly outperforms the state-of-the-art saliency detection approaches.

Connected through hyperedge HE-1 (induced by source paper "super diffusion for salient object detection"):
  with members:
    - "saliency integration an arbitrator model"
    - "deeply supervised salient object detection with short connections"
    - "deep visual attention prediction"
    - "hierarchical cellular automata for visual saliency"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "salient object detection a discriminative regional feature integration approach"
    - "deep contrast learning for salient object detection"
    - "salient object detection a benchmark"

Connected through hyperedge HE-2 (induced by source paper "salient object detection a survey"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "instance level salient object segmentation"
    - "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"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "what makes for effective detection proposals"
    - "deepsaliency multi task deep neural network model for salient object detection"

Connected through hyperedge HE-3 (induced by source paper "masking salient object detection a mask region based convolutional neural network analysis for segmentation of salient objects"):
  with members:
    - "salient object detection with lossless feature reflection and weighted structural loss"
    - "a simple pooling based design for real time salient object detection"
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "salient object detection by lossless feature reflection"
    - "robust iris segmentation based on fully convolutional networks and generative adversarial networks"
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "weakly supervised salient object detection using image labels"

Connected through hyperedge HE-4 (induced by source paper "edge guided non local fully convolutional network for salient object detection"):
  with members:
    - "a simple pooling based design for real time salient object detection"
    - "deeply supervised salient object detection with short connections"
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "edge preserving and multi scale contextual neural network for salient object detection"
    - "deep edge aware saliency detection"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "visual tracking via dynamic graph learning"
    - "amulet aggregating multi level convolutional features for salient object detection"

Connected through hyperedge HE-5 (induced by source paper "reverse attention for salient object detection"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "deep laplacian pyramid networks for fast and accurate super resolution"
    - "learning to refine object contours with a top down fully convolutional encoder decoder network"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "squeeze and excitation networks"
    - "srn side output residual network for object symmetry detection in the wild"
    - "instance level salient object segmentation"

Connected through hyperedge HE-6 (induced by source paper "video smoke detection based on deep saliency network"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "multi evidence filtering and fusion for multi label classification object detection and semantic segmentation based on weakly supervised learning"
    - "rgbd salient object detection via deep fusion"
    - "what can help pedestrian detection"
    - "deep domain adaptation based video smoke detection using synthetic smoke images"
    - "hide and seek forcing a network to be meticulous for weakly supervised object and action localization"
    - "disc deep image saliency computing via progressive representation learning"
    - "deep contrast learning for salient object detection"

Connected through hyperedge HE-7 (induced by source paper "video salient object detection via fully convolutional networks"):
  with members:
    - "deep cropping via attention box prediction and aesthetics assessment"
    - "deep contrast learning for salient object detection"
    - "spatio temporal saliency networks for dynamic saliency prediction"
    - "learning video object segmentation from static images"
    - "two stream convolutional networks for dynamic saliency prediction"
    - "deepsaliency multi task deep neural network model for salient object detection"
    - "personalizing human video pose estimation"
    - "visual saliency based on multiscale deep features"

Connected through hyperedge HE-8 (induced by source paper "self explanatory deep salient object detection"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "object region mining with adversarial erasing a simple classification to semantic segmentation approach"
    - "learning important features through propagating activation differences"
    - "instance level salient object segmentation"
    - "exploiting saliency for object segmentation from image level labels"
    - "visualizing deep neural network decisions prediction difference analysis"
    - "deep saliency with encoded low level distance map and high level features"

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)