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: "object skeleton extraction in natural images by fusing scale associated deep side outputs"
  Abstract: Object skeleton is a useful cue for object detection, complementary to the object contour, as it provides a structural representation to describe the relationship among object parts. While object skeleton extraction in natural images is a very challenging problem, as it requires the extractor to be able to capture both local and global image context to determine the intrinsic scale of each skeleton pixel. Existing methods rely on per-pixel based multi-scale feature computation, which results in difficult modeling and high time consumption. In this paper, we present a fully convolutional network with multiple scale-associated side outputs to address this problem. By observing the relationship between the receptive field sizes of the sequential stages in the network and the skeleton scales they can capture, we introduce a scale-associated side output to each stage. We impose supervision to different stages by guiding the scale-associated side outputs toward groundtruth skeletons of different scales. The responses of the multiple scale-associated side outputs are then fused in a scale-specific way to localize skeleton pixels with multiple scales effectively. Our method achieves promising results on two skeleton extraction datasets, and significantly outperforms other competitors.

Connected through hyperedge HE-1 (induced by source paper "detecting multi oriented text with corner based region proposals"):
  with members:
    - "arbitrary oriented scene text detection via rotation proposals"
    - "deep matching prior network toward tighter multi oriented text detection"
    - "deep extreme cut from extreme points to object segmentation"
    - "east an efficient and accurate scene text detector"
    - "oriented response networks"
    - "extreme clicking for efficient object annotation"
    - "single shot text detector with regional attention"
    - "fused text segmentation networks for multi oriented scene text detection"

Connected through hyperedge HE-2 (induced by source paper "occlusion shared and feature separated network for occlusion relationship reasoning"):
  with members:
    - "context constrained accurate contour extraction for occlusion edge detection"
    - "a novel multi layer framework for tiny obstacle discovery"
    - "deepflux for skeletons in the wild"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "dilated residual networks"
    - "understanding convolution for semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-3 (induced by source paper "skeletonnet shape pixel to skeleton pixel"):
  with members:
    - "three birds one stone a unified framework for salient object segmentation edge detection and skeleton extraction"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"
    - "holistically nested edge detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "hypercolumns for object segmentation and fine grained localization"
    - "deepedge a multi scale bifurcated deep network for top down contour detection"
    - "fast edge detection using structured forests"

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 "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"
    - "deepskeleton learning multi task scale associated deep side outputs for object skeleton extraction in natural images"

Connected through hyperedge HE-6 (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"
    - "deepskeleton learning multi task scale associated deep side outputs for object skeleton extraction in natural images"
    - "masklab instance segmentation by refining object detection with semantic and direction features"

Connected through hyperedge HE-7 (induced by source paper "srn side output residual network for object reflection symmetry detection and beyond"):
  with members:
    - "sparse coding on cascaded residuals"
    - "srn side output residual network for object symmetry detection in the wild"
    - "deepskeleton learning multi task scale associated deep side outputs for object skeleton extraction in natural images"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "ubernet training a universal convolutional neural network for low mid and high level vision using diverse datasets and limited memory"
    - "hyperface a deep multi task learning framework for face detection landmark localization pose estimation and gender recognition"
    - "object contour detection with a fully convolutional encoder decoder network"
    - "laplacian pyramid reconstruction and refinement for semantic segmentation"

Connected through hyperedge HE-8 (induced by source paper "linear span network for object skeleton detection"):
  with members:
    - "srn side output residual network for object symmetry detection in the wild"
    - "deepskeleton learning multi task scale associated deep side outputs for object skeleton extraction in natural images"
    - "convolutional pose machines"
    - "holistically nested edge detection"
    - "very deep convolutional networks for large scale image recognition"
    - "fast edge detection using structured forests"

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)