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 "road crack detection using deep convolutional neural network and adaptive thresholding"):
  Abstract: Crack is one of the most common road distresses which may pose road safety hazards. Generally, crack detection is performed by either certified inspectors or structural engineers. This task is, however, time-consuming, subjective and labor-intensive. In this paper, we propose a novel road crack detection algorithm based on deep learning and adaptive image segmentation. Firstly, a deep convolutional neural network is trained to determine whether an image contains cracks or not. The images containing cracks are then smoothed using bilateral filtering, which greatly minimizes the number of noisy pixels. Finally, we utilize an adaptive thresholding method to extract the cracks from road surface. The experimental results illustrate that our network can classify images with an accuracy of 99.92%, and the cracks can be successfully extracted from the images using our proposed thresholding algorithm.

Includes members:
  Member "using dp towards a shortest path problem related application":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "key ingredients of self driving cars")
  Member "real time dense stereo embedded in a uav for road inspection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "pt resnet perspective transformation based residual network for semantic road image segmentation")
  Member "real time subpixel fast bilateral stereo":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust lane marking detection algorithm using drivable area segmentation and extended slt")
  Member "road surface 3d reconstruction based on dense subpixel disparity map estimation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fast depth estimation for view synthesis")
  Member "real time stereo vision based lane detection system":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "pt resnet perspective transformation based residual network for semantic road image segmentation")
  Member "real time stereo vision for road surface 3 d reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "key ingredients of self driving cars")
  Member "a novel disparity transformation algorithm for road segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "key ingredients of self driving cars")
  Member "improving computer aided detection using convolutional neural networks and random view aggregation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "elixirnet relation aware network architecture adaptation for medical lesion detection")

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)