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: "inner and inter label propagation salient object detection in the wild"
  Abstract: In this paper, we propose a novel label propagation based method for saliency detection. A key observation is that saliency in an image can be estimated by propagating the labels extracted from the most certain background and object regions. For most natural images, some boundary superpixels serve as the background labels and the saliency of other superpixels are determined by ranking their similarities to the boundary labels based on an inner propagation scheme. For images of complex scenes, we further deploy a 3-cue-center-biased objectness measure to pick out and propagate foreground labels. A co-transduction algorithm is devised to fuse both boundary and objectness labels based on an inter propagation scheme. The compactness criterion decides whether the incorporation of objectness labels is necessary, thus greatly enhancing computational efficiency. Results on five benchmark datasets with pixel-wise accurate annotations show that the proposed method achieves superior performance compared with the newest state-of-the-arts in terms of different evaluation metrics.

Connected through hyperedge HE-1 (induced by source paper "edge preserving and multi scale contextual neural network for salient object detection"):
  with members:
    - "salient object detection a survey"
    - "stc a simple to complex framework for weakly supervised semantic segmentation"
    - "disc deep image saliency computing via progressive representation learning"
    - "deepsaliency multi task deep neural network model for salient object detection"
    - "holistically nested edge detection"
    - "visual saliency based on multiscale deep features"
    - "instance aware semantic segmentation via multi task network cascades"
    - "salient object detection a benchmark"

Connected through hyperedge HE-2 (induced by source paper "person search in videos with one portrait through visual and temporal links"):
  with members:
    - "person recognition in personal photo collections"
    - "unifying identification and context learning for person recognition"
    - "person search with natural language description"
    - "detecting temporally consistent objects in videos through object class label propagation"
    - "deep attributes driven multi camera person re identification"
    - "learning deep feature representations with domain guided dropout for person re identification"
    - "a comprehensive evaluation and benchmark for person re identification features metrics and datasets"
    - "joint face detection and alignment using multitask cascaded convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "exploiting the value of the center dark channel prior for salient object detection"):
  with members:
    - "salient object detection a survey"
    - "pdnet prior model guided depth enhanced network for salient object detection"
    - "automatic salient object detection for panoramic images using region growing and fixation prediction model"
    - "instance level salient object segmentation"
    - "amulet aggregating multi level convolutional features for salient object detection"
    - "a benchmark dataset and saliency guided stacked autoencoders for video based salient object detection"
    - "visual saliency based on multiscale deep features"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-4 (induced by source paper "acquire augment segment enjoy weakly supervised instance segmentation of supermarket products"):
  with members:
    - "mvtec d2s densely segmented supermarket dataset"
    - "learning to segment every thing"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "simple does it weakly supervised instance and semantic segmentation"
    - "amodal instance segmentation"
    - "semantic amodal segmentation"
    - "salient object detection a benchmark"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-5 (induced by source paper "co saliency detection for rgbd images based on multi constraint feature matching and cross label propagation"):
  with members:
    - "saliency detection for stereoscopic images based on depth confidence analysis and multiple cues fusion"
    - "learning multilayer channel features for pedestrian detection"
    - "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"
    - "salient object detection a benchmark"
    - "very deep convolutional networks for large scale image recognition"
    - "learning rich features from rgb d images for object detection and segmentation"

Connected through hyperedge HE-6 (induced by source paper "pdnet prior model guided depth enhanced network for salient object detection"):
  with members:
    - "salient object detection a survey"
    - "a multilayer backpropagation saliency detection algorithm and its applications"
    - "deep saliency with encoded low level distance map and high level features"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-7 (induced by source paper "a multilayer backpropagation saliency detection algorithm and its applications"):
  with members:
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

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)