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: "deepskeleton learning multi task scale associated deep side outputs for object skeleton extraction in natural images"
  Abstract: Object skeletons are useful for object representation and object detection. They are complementary to the object contour, and provide extra information, such as how object scale (thickness) varies among object parts. But object skeleton extraction from natural images is very challenging, because it requires the extractor to be able to capture both local and non-local image context in order to determine the scale of each skeleton pixel. In this paper, we present a novel fully convolutional network with multiple scale-associated side outputs to address this problem. By observing the relationship between the receptive field sizes of the different layers in the network and the skeleton scales they can capture, we introduce two scale-associated side outputs to each stage of the network. The network is trained by multi-task learning, where one task is skeleton localization to classify whether a pixel is a skeleton pixel or not, and the other is skeleton scale prediction to regress the scale of each skeleton pixel. Supervision is imposed at different stages by guiding the scale-associated side outputs toward the ground-truth skeletons at the appropriate scales. The responses of the multiple scale-associated side outputs are then fused in a scale-specific way to detect skeleton pixels using multiple scales effectively. Our method achieves promising results on two skeleton extraction datasets, and significantly outperforms other competitors. In addition, the usefulness of the obtained skeletons and scales (thickness) are verified on two object detection applications: foreground object segmentation and object proposal detection.

Connected through hyperedge HE-1 (induced by source paper "predicting animation skeletons for 3d articulated models via volumetric nets"):
  with members:
    - "3d hand shape and pose estimation from a single rgb image"
    - "p2p net bidirectional point displacement net for shape transform"
    - "jointly optimize data augmentation and network training adversarial data augmentation in human pose estimation"
    - "deepflux for skeletons in the wild"
    - "adaptive o cnn a patch based deep representation of 3d shapes"
    - "hi fi hierarchical feature integration for skeleton detection"
    - "linear span network for object skeleton detection"
    - "amat medial axis transform for natural images"

Connected through hyperedge HE-2 (induced by source paper "deep distance transform for tubular structure segmentation in ct scans"):
  with members:
    - "one network to segment them all a general lightweight system for accurate 3d medical image segmentation"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "nnu net breaking the spell on successful medical image segmentation"
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "kid net convolution networks for kidney vessels segmentation from ct volumes"
    - "deepflux for skeletons in the wild"
    - "3d semi supervised learning with uncertainty aware multi view co training"
    - "training multi organ segmentation networks with sample selection by relaxed upper confident bound"

Connected through hyperedge HE-3 (induced by source paper "skelneton 2019 dataset and challenge on deep learning for geometric shape understanding"):
  with members:
    - "p2p net bidirectional point displacement net for shape transform"
    - "guided proceduralization optimizing geometry processing and grammar extraction for architectural models"
    - "the 2018 davis challenge on video object segmentation"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "semantic scene completion from a single depth image"
    - "learning a probabilistic latent space of object shapes via 3d generative adversarial modeling"
    - "shapenet an information rich 3d model repository"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "three birds one stone a general architecture for salient object segmentation edge detection and skeleton extraction"):
  with members:
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "visual saliency prediction using a mixture of deep neural networks"
    - "learning uncertain convolutional features for accurate saliency detection"
    - "rgbd salient object detection via deep fusion"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "richer convolutional features for edge detection"
    - "srn side output residual network for object symmetry detection in the wild"

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 "deep smoke segmentation"):
  with members:
    - "a cnn rnn architecture for multi label weather recognition"
    - "deeply supervised salient object detection with short connections"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "video salient object detection via fully convolutional networks"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "lstm fully convolutional networks for time series classification"
    - "going deeper with contextual cnn for hyperspectral image classification"

Connected through hyperedge HE-7 (induced by source paper "three birds one stone a unified framework for salient object segmentation edge detection and skeleton extraction"):
  with members:
    - "salient object detection a survey"
    - "deeply supervised salient object detection with short connections"
    - "video salient object detection via fully convolutional networks"
    - "rgbd salient object detection via deep fusion"
    - "richer convolutional features for edge detection"
    - "srn side output residual network for object symmetry detection in the wild"
    - "instance level salient object segmentation"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"

Connected through hyperedge HE-8 (induced by source paper "deepflux for skeletons in the wild"):
  with members:
    - "hi fi hierarchical feature integration for skeleton detection"
    - "linear span network for object skeleton detection"
    - "amat medial axis transform for natural images"
    - "richer convolutional features for edge detection"
    - "srn side output residual network for object symmetry detection in the wild"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "masklab instance segmentation by refining object detection with semantic and direction features"
    - "convolutional oriented boundaries from image segmentation to high level tasks"

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)