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: "pothole detection based on disparity transformation and road surface modeling"
  Abstract: Pothole detection is one of the most important tasks for road maintenance. Computer vision approaches are generally based on either 2D road image analysis or 3D road surface modeling. However, these two categories are always used independently. Furthermore, the pothole detection accuracy is still far from satisfactory. Therefore, in this paper, we present a robust pothole detection algorithm that is both accurate and computationally efficient. A dense disparity map is first transformed to better distinguish between damaged and undamaged road areas. To achieve greater disparity transformation efficiency, golden section search and dynamic programming are utilized to estimate the transformation parameters. Otsu's thresholding method is then used to extract potential undamaged road areas from the transformed disparity map. The disparities in the extracted areas are modeled by a quadratic surface using least squares fitting. To improve disparity map modeling robustness, the surface normal is also integrated into the surface modeling process. Furthermore, random sample consensus is utilized to reduce the effects caused by outliers. By comparing the difference between the actual and modeled disparity maps, the potholes can be detected accurately. Finally, the point clouds of the detected potholes are extracted from the reconstructed 3D road surface. The experimental results show that the successful detection accuracy of the proposed system is around 98.7% and the overall pixel-level accuracy is approximately 99.6%.

Connected through hyperedge HE-1 (induced by source paper "pt resnet perspective transformation based residual network for semantic road image segmentation"):
  with members:
    - "a robust pavement mapping system based on normal constrained stereo visual odometry"
    - "real time dense stereo embedded in a uav for road inspection"
    - "key ingredients of self driving cars"
    - "road damage detection based on unsupervised disparity map segmentation"
    - "scene understanding networks for autonomous driving based on around view monitoring system"
    - "a general pipeline for 3d detection of vehicles"
    - "road surface 3d reconstruction based on dense subpixel disparity map estimation"
    - "real time stereo vision based lane detection system"

Connected through hyperedge HE-2 (induced by source paper "a robust pavement mapping system based on normal constrained stereo visual odometry"):
  with members:
    - "a robust roll angle estimation algorithm based on gradient descent"
    - "real time dense stereo embedded in a uav for road inspection"
    - "key ingredients of self driving cars"
    - "road damage detection based on unsupervised disparity map segmentation"
    - "road surface 3d reconstruction based on dense subpixel disparity map estimation"
    - "a novel disparity transformation algorithm for road segmentation"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"

Connected through hyperedge HE-3 (induced by source paper "robust lane marking detection algorithm using drivable area segmentation and extended slt"):
  with members:
    - "real time dense stereo embedded in a uav for road inspection"
    - "key ingredients of self driving cars"
    - "road damage detection based on unsupervised disparity map segmentation"
    - "real time subpixel fast bilateral stereo"
    - "road surface 3d reconstruction based on dense subpixel disparity map estimation"
    - "real time stereo vision based lane detection system"
    - "a novel disparity transformation algorithm for road segmentation"
    - "multiple lane detection algorithm based on optimised dense disparity map estimation"

Connected through hyperedge HE-4 (induced by source paper "low cost gps aided lidar state estimation and map building"):
  with members:
    - "key ingredients of self driving cars"
    - "road damage detection based on unsupervised disparity map segmentation"
    - "road surface 3d reconstruction based on dense subpixel disparity map estimation"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "robust and precise vehicle localization based on multi sensor fusion in diverse city scenes"
    - "on manifold preintegration for real time visual inertial odometry"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-5 (induced by source paper "road damage detection based on unsupervised disparity map segmentation"):
  with members:
    - "real time dense stereo embedded in a uav for road inspection"
    - "road surface 3d reconstruction based on dense subpixel disparity map estimation"
    - "real time stereo vision based lane detection system"
    - "real time stereo vision for road surface 3 d reconstruction"
    - "a novel disparity transformation algorithm for road segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "autonomous uav landing system based on visual navigation"):
  with members:
    - "real time dense stereo embedded in a uav for road inspection"
    - "key ingredients of self driving cars"
    - "road damage detection based on unsupervised disparity map segmentation"
    - "road surface 3d reconstruction based on dense subpixel disparity map estimation"

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)