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 extrinsic calibration for lidar stereo vehicle sensor setups"
  Abstract: Sensor setups consisting of a combination of 3D range scanner lasers and stereo vision systems are becoming a popular choice for on-board perception systems in vehicles; however, the combined use of both sources of information implies a tedious calibration process. We present a method for extrinsic calibration of lidar-stereo camera pairs without user intervention. Our calibration approach is aimed to cope with the constraints commonly found in automotive setups, such as low-resolution and specific sensor poses. To demonstrate the performance of our method, we also introduce a novel approach for the quantitative assessment of the calibration results, based on a simulation environment. Tests using real devices have been conducted as well, proving the usability of the system and the improvement over the existing approaches. Code is available at this http URL

Connected through hyperedge HE-1 (induced by source paper "soic semantic online initialization and calibration for lidar and camera"):
  with members:
    - "spatiotemporal camera lidar calibration a targetless and structureless approach"
    - "2d3d matchnet learning to match keypoints across 2d image and 3d point cloud"
    - "calibnet geometrically supervised extrinsic calibration using 3d spatial transformer networks"
    - "improving semantic segmentation via video propagation and label relaxation"
    - "pointrcnn 3d object proposal generation and detection from point cloud"
    - "joint 3d proposal generation and object detection from view aggregation"
    - "reflectance intensity assisted automatic and accurate extrinsic calibration of 3d lidar and panoramic camera using a printed chessboard"
    - "frustum pointnets for 3d object detection from rgb d data"

Connected through hyperedge HE-2 (induced by source paper "spatiotemporal camera lidar calibration a targetless and structureless approach"):
  with members:
    - "robust photogeometric localization over time for map centric loop closure"
    - "probabilistic surfel fusion for dense lidar mapping"
    - "elastic lidar fusion dense map centric continuous time slam"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-3 (induced by source paper "improvements to target based 3d lidar to camera calibration"):
  with members:
    - "a novel dual lidar calibration algorithm using planar surfaces"
    - "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"
    - "lidar camera calibration using 3d 3d point correspondences"
    - "faster and better a machine learning approach to corner detection"

Connected through hyperedge HE-4 (induced by source paper "calibnet geometrically supervised extrinsic calibration using 3d spatial transformer networks"):
  with members:
    - "sbnet sparse blocks network for fast inference"
    - "unsupervised learning of depth and ego motion from video"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "regnet multimodal sensor registration using deep neural networks"
    - "undeepvo monocular visual odometry through unsupervised deep learning"
    - "stacked hourglass networks for human pose estimation"
    - "a point set generation network for 3d object reconstruction from a single image"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "calibnet self supervised extrinsic calibration using 3d spatial transformer networks"):
  with members:
    - "sbnet sparse blocks network for fast inference"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "lidar camera calibration using 3d 3d point correspondences"
    - "regnet multimodal sensor registration using deep neural networks"
    - "undeepvo monocular visual odometry through unsupervised deep learning"
    - "stacked hourglass networks for human pose estimation"
    - "a point set generation network for 3d object reconstruction from a single image"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "robust fusion of lidar and wide angle camera data for autonomous mobile robots"):
  with members:
    - "smart guiding glasses for visually impaired people in indoor environment"
    - "free space detection with self supervised and online trained fully convolutional networks"

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)