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 "pt resnet perspective transformation based residual network for semantic road image segmentation"):
  Abstract: Semantic road region segmentation is a high-level task, which paves the way towards road scene understanding. This paper presents a residual network trained for semantic road segmentation. Firstly, we represent the projections of road disparities in the v-disparity map as a linear model, which can be estimated by optimizing the v-disparity map using dynamic programming. This linear model is then utilized to reduce the redundant information in the left and right road images. The right image is also transformed into the left perspective view, which greatly enhances the road surface similarity between the two images. Finally, the processed stereo images and their disparity maps are concatenated to create a set of 3D images, which are then utilized to train our neural network. The experimental results illustrate that our network achieves a maximum F1-measure of approximately 91.19% when analyzing the images from the KITTI road dataset.

Includes members:
  Member "pothole detection based on disparity transformation and road surface modeling":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a robust pavement mapping system based on normal constrained stereo visual odometry")
  Member "a robust pavement mapping system based on normal constrained stereo visual odometry":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "real time environmentally robust 3d lidar localization")
  Member "real time dense stereo embedded in a uav for road inspection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a robust pavement mapping system based on normal constrained stereo visual odometry")
  Member "key ingredients of self driving cars":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "attacking vision based perception in end to end autonomous driving models")
  Member "road damage detection based on unsupervised disparity map segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a robust pavement mapping system based on normal constrained stereo visual odometry")
  Member "scene understanding networks for autonomous driving based on around view monitoring system":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "universal semantic segmentation for fisheye urban driving images")
  Member "a general pipeline for 3d detection of vehicles":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "from points to parts 3d object detection from point cloud with part aware and part aggregation network")
  Member "road surface 3d reconstruction based on dense subpixel disparity map estimation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "fast depth estimation for view synthesis")

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)