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 "a fractal based cnn for detecting complicated curves in afm images"):
  Abstract: Convolutional neural networks (CNNs) have been widely used in computer vision, including low-and middle-level vision problems such as contour detection and image reconstruction. In this paper, we propose a novel fractal-based CNN model for the problem of complicated curve detection, providing a geometric interpretation of CNN-based image model leveraging local fractal analysis. Utilizing the notion of local self-similarity, we develop a local fractal model for images. A curve detector is designed based on the model, consisting of an orientation-adaptive filtering process to enhance the local response along a certain orientation. This is followed by a post-processing step to preserve local invariance of the fractal dimension of image. We show the iterative framework of such an adaptive filtering process can be re-instantiated approximately via a CNN model, the nonlinear processing layer of which preserves fractal dimension approximately and the convolution layer achieves orientation enhancement. We demonstrate our fractal-based CNN model on the challenging task for detecting complicated curves from the texture-like images depicting microstructures of materials obtained by atomic force microscopy (AFM).

Includes members:
  Member "holistically nested edge detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep multicameral decoding for localizing unoccluded object instances from a single rgb image")
  Member "shadow optimization from structured deep edge detection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards ghost free shadow removal via dual hierarchical aggregation network and shadow matting gan")
  Member "deepedge a multi scale bifurcated deep network for top down contour detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cafenet class agnostic few shot edge detection network")
  Member "deformable part models are convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "starnet towards weakly supervised few shot detection and explainable few shot classification")
  Member "fast edge detection using structured forests":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "saliency enhancement using gradient domain edges merging")
  Member "rich feature hierarchies for accurate object detection and semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "relatext exploiting visual relationships for arbitrary shaped scene text detection with graph convolutional networks")
  Member "invariant scattering convolution networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "spatially adaptive filter units for compact and efficient deep neural networks")

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)