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: "a novel dual lidar calibration algorithm using planar surfaces"
  Abstract: Multiple lidars are prevalently used on mobile vehicles for rendering a broad view to enhance the performance of localization and perception systems. However, precise calibration of multiple lidars is challenging since the feature correspondences in scan points cannot always provide enough constraints. To address this problem, the existing methods require fixed calibration targets in scenes or rely exclusively on additional sensors. In this paper, we present a novel method that enables automatic lidar calibration without these restrictions. Three linearly independent planar surfaces appearing in surroundings is utilized to find correspondences. Two components are developed to ensure the extrinsic parameters to be found: a closed-form solver for initialization and an optimizer for refinement by minimizing a nonlinear cost function. Simulation and experimental results demonstrate the high accuracy of our calibration approach with the rotation and translation errors smaller than 0.05rad and 0.1m respectively.

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"
    - "road surface 3d reconstruction based on dense subpixel disparity map estimation"
    - "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-2 (induced by source paper "improvements to target based 3d lidar to camera calibration"):
  with members:
    - "automatic extrinsic calibration between a camera and a 3d lidar using 3d point and plane correspondences"
    - "lidartag a real time fiducial tag using point clouds"
    - "feedback control of a cassie bipedal robot walking standing and riding a segway"
    - "automatic extrinsic calibration for lidar stereo vehicle sensor setups"
    - "lidar camera calibration using 3d 3d point correspondences"
    - "faster and better a machine learning approach to corner detection"

Connected through hyperedge HE-3 (induced by source paper "automatic calibration of dual lidars using two poles stickered with retro reflective tape"):
  with members:
    - "automatic calibration of multiple 3d lidars in urban environments"
    - "low cost gps aided lidar state estimation and map building"
    - "real time environmentally robust 3d lidar localization"
    - "key ingredients of self driving cars"

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)