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: "real time stereo vision for road surface 3 d reconstruction"
  Abstract: Stereo vision techniques have been widely used in civil engineering to acquire 3-D road data. The two important factors of stereo vision are accuracy and speed. However, it is very challenging to achieve both of them simultaneously and therefore the main aim of developing a stereo vision system is to improve the trade-off between these two factors. In this paper, we present a real-time stereo vision system used for road surface 3-D reconstruction. The proposed system is developed from our previously published 3-D reconstruction algorithm where the perspective view of the target image is first transformed into the reference view, which not only increases the disparity accuracy but also improves the processing speed. Then, the correlation cost between each pair of blocks is computed and stored in two 3-D cost volumes. To adaptively aggregate the matching costs from neighbourhood systems, bilateral filtering is performed on the cost volumes. This greatly reduces the ambiguities during stereo matching and further improves the precision of the estimated disparities. Finally, the subpixel resolution is achieved by conducting a parabola interpolation and the subpixel disparity map is used to reconstruct the 3-D road surface. The proposed algorithm is implemented on an NVIDIA GTX 1080 GPU for the real-time purpose. The experimental results illustrate that the reconstruction accuracy is around 3 mm.

Connected through hyperedge HE-1 (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"
    - "road surface 3d reconstruction based on dense subpixel disparity map estimation"
    - "trafficpredict trajectory prediction for heterogeneous traffic agents"
    - "a novel disparity transformation algorithm for road segmentation"

Connected through hyperedge HE-2 (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"
    - "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"
    - "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-3 (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"
    - "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"
    - "rethinking atrous convolution for semantic image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "a robust roll angle estimation algorithm based on gradient descent"):
  with members:
    - "real time dense stereo embedded in a uav for road inspection"
    - "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-5 (induced by source paper "real time dense stereo embedded in a uav for road inspection"):
  with members:
    - "road surface 3d reconstruction based on dense subpixel disparity map estimation"
    - "a novel disparity transformation algorithm for road segmentation"
    - "pyramid stereo matching network"
    - "computing the stereo matching cost with a convolutional neural network"
    - "learning to compare image patches via convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "multiple lane detection algorithm based on optimised dense disparity map estimation"):
  with members:
    - "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"

Connected through hyperedge HE-7 (induced by source paper "mobile robot localisation and navigation using lego nxt and ultrasonic sensor"):
  with members:
    - "road surface 3d reconstruction based on dense subpixel disparity map estimation"
    - "real time stereo vision based lane detection system"
    - "autonomous mobile robot navigation in uneven and unstructured indoor environments"
    - "sampling based algorithms for optimal motion planning"

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)