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: "automatic calibration of multiple 3d lidars in urban environments"
  Abstract: Multiple LiDARs have progressively emerged on autonomous vehicles for rendering a wide field of view and dense measurements. However, the lack of precise calibration negatively affects their potential applications in localization and perception systems. In this paper, we propose a novel system that enables automatic multi-LiDAR calibration without any calibration target, prior environmental information, and initial values of the extrinsic parameters. Our approach starts with a hand-eye calibration for automatic initialization by aligning the estimated motions of each sensor. The resulting parameters are then refined with an appearance-based method by minimizing a cost function constructed from point-plane correspondences. Experimental results on simulated and real-world data sets demonstrate the reliability and accuracy of our calibration approach. The proposed approach can calibrate a multi-LiDAR system with the rotation and translation errors less than 0.04 [rad] and 0.1 [m] respectively for a mobile platform.

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"
    - "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"
    - "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 "automatic calibration of dual lidars using two poles stickered with retro reflective tape"):
  with members:
    - "low cost gps aided lidar state estimation and map building"
    - "real time environmentally robust 3d lidar localization"
    - "key ingredients of self driving cars"
    - "a novel dual lidar calibration algorithm using planar surfaces"

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.RO(Robotics)