human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "deepflux for skeletons in the wild"):
  Abstract: Computing object skeletons in natural images is challenging, owing to large variations in object appearance and scale, and the complexity of handling background clutter. Many recent methods frame object skeleton detection as a binary pixel classification problem, which is similar in spirit to learning-based edge detection, as well as to semantic segmentation methods. In the present article, we depart from this strategy by training a CNN to predict a two-dimensional vector field, which maps each scene point to a candidate skeleton pixel, in the spirit of flux-based skeletonization algorithms. This "image context flux" representation has two major advantages over previous approaches. First, it explicitly encodes the relative position of skeletal pixels to semantically meaningful entities, such as the image points in their spatial context, and hence also the implied object boundaries. Second, since the skeleton detection context is a region-based vector field, it is better able to cope with object parts of large width. We evaluate the proposed method on three benchmark datasets for skeleton detection and two for symmetry detection, achieving consistently superior performance over state-of-the-art methods.

Includes members:
  Member "hi fi hierarchical feature integration for skeleton detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "res2net a new multi scale backbone architecture")
  Member "linear span network for object skeleton detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "predicting animation skeletons for 3d articulated models via volumetric nets")
  Member "amat medial axis transform for natural images":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "predicting animation skeletons for 3d articulated models via volumetric nets")
  Member "richer convolutional features for edge detection":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "cafenet class agnostic few shot edge detection network")
  Member "srn side output residual network for object symmetry detection in the wild":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "min entropy latent model for weakly supervised object detection")
  Member "understanding the effective receptive field in deep convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "joint anchor feature refinement for real time accurate object detection in images and videos")
  Member "deepskeleton learning multi task scale associated deep side outputs for object skeleton extraction in natural images":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "predicting animation skeletons for 3d articulated models via volumetric nets")
  Member "masklab instance segmentation by refining object detection with semantic and direction features":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "weakly supervised instance segmentation by deep community learning")

Question: Please tell me which class the source paper 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)