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: "road surface 3d reconstruction based on dense subpixel disparity map estimation"
  Abstract: Various 3D reconstruction methods have enabled civil engineers to detect damage on a road surface. To achieve the millimeter accuracy required for road condition assessment, a disparity map with subpixel resolution needs to be used. However, none of the existing stereo matching algorithms are specially suitable for the reconstruction of the road surface. Hence in this paper, we propose a novel dense subpixel disparity estimation algorithm with high computational efficiency and robustness. This is achieved by first transforming the perspective view of the target frame into the reference view, which not only increases the accuracy of the block matching for the road surface but also improves the processing speed. The disparities are then estimated iteratively using our previously published algorithm, where the search range is propagated from three estimated neighboring disparities. Since the search range is obtained from the previous iteration, errors may occur when the propagated search range is not sufficient. Therefore, a correlation maxima verification is performed to rectify this issue, and the subpixel resolution is achieved by conducting a parabola interpolation enhancement. Furthermore, a novel disparity global refinement approach developed from the Markov random fields and fast bilateral stereo is introduced to further improve the accuracy of the estimated disparity map, where disparities are updated iteratively by minimizing the energy function that is related to their interpolated correlation polynomials. The algorithm is implemented in C language with a near real-time performance. The experimental results illustrate that the absolute error of the reconstruction varies from 0.1 to 3 mm.

Connected through hyperedge HE-1 (induced by source paper "fast depth estimation for view synthesis"):
  with members:
    - "ga net guided aggregation net for end to end stereo matching"
    - "semi dense 3d reconstruction with a stereo event camera"
    - "fast disparity estimation using dense networks"
    - "pyramid stereo matching network"
    - "stacked hourglass networks for human pose estimation"
    - "perceptual losses for real time style transfer and super resolution"
    - "a large dataset to train convolutional networks for disparity optical flow and scene flow estimation"
    - "densely connected convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "key ingredients of self driving cars"):
  with members:
    - "road crack detection using deep convolutional neural network and adaptive thresholding"
    - "automatic calibration of multiple 3d lidars in urban environments"
    - "using dp towards a shortest path problem related application"
    - "tightly coupled 3d lidar inertial odometry and mapping"
    - "a novel dual lidar calibration algorithm using planar surfaces"
    - "trafficpredict trajectory prediction for heterogeneous traffic agents"
    - "real time stereo vision for road surface 3 d reconstruction"
    - "a novel disparity transformation algorithm for road segmentation"

Connected through hyperedge HE-3 (induced by source paper "pt resnet perspective transformation based residual network for semantic road image segmentation"):
  with members:
    - "pothole detection based on disparity transformation and road surface modeling"
    - "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"
    - "real time stereo vision based lane detection system"

Connected through hyperedge HE-4 (induced by source paper "a robust pavement mapping system based on normal constrained stereo visual odometry"):
  with members:
    - "pothole detection based on disparity transformation and road surface modeling"
    - "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"
    - "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-5 (induced by source paper "robust lane marking detection algorithm using drivable area segmentation and extended slt"):
  with members:
    - "pothole detection based on disparity transformation and road surface modeling"
    - "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"
    - "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-6 (induced by source paper "road crack detection using deep convolutional neural network and adaptive thresholding"):
  with members:
    - "using dp towards a shortest path problem related application"
    - "real time dense stereo embedded in a uav for road inspection"
    - "real time subpixel fast bilateral stereo"
    - "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"
    - "improving computer aided detection using convolutional neural networks and random view aggregation"
    - "improving object detection with deep convolutional networks via bayesian optimization and structured prediction"

Connected through hyperedge HE-7 (induced by source paper "low cost gps aided lidar state estimation and map building"):
  with members:
    - "pothole detection based on disparity transformation and road surface modeling"
    - "key ingredients of self driving cars"
    - "road damage detection based on unsupervised disparity map segmentation"
    - "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-8 (induced by source paper "road damage detection based on unsupervised disparity map segmentation"):
  with members:
    - "pothole detection based on disparity transformation and road surface modeling"
    - "real time dense stereo embedded in a uav for road inspection"
    - "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"

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)