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: "lidar camera calibration using 3d 3d point correspondences"
  Abstract: With the advent of autonomous vehicles, LiDAR and cameras have become an indispensable combination of sensors. They both provide rich and complementary data which can be used by various algorithms and machine learning to sense and make vital inferences about the surroundings. We propose a novel pipeline and experimental setup to find accurate rigid-body transformation for extrinsically calibrating a LiDAR and a camera. The pipeling uses 3D-3D point correspondences in LiDAR and camera frame and gives a closed form solution. We further show the accuracy of the estimate by fusing point clouds from two stereo cameras which align perfectly with the rotation and translation estimated by our method, confirming the accuracy of our method's estimates both mathematically and visually. Taking our idea of extrinsic LiDAR-camera calibration forward, we demonstrate how two cameras with no overlapping field-of-view can also be calibrated extrinsically using 3D point correspondences. The code has been made available as open-source software in the form of a ROS package, more information about which can be sought here: this https URL .

Connected through hyperedge HE-1 (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"
    - "automatic extrinsic calibration for lidar stereo vehicle sensor setups"
    - "faster and better a machine learning approach to corner detection"

Connected through hyperedge HE-2 (induced by source paper "line based camera pose estimation in point cloud of structured environments"):
  with members:
    - "fast 3d line segment detection from unorganized point cloud"
    - "efficient continuous time slam for 3d lidar based online mapping"
    - "learning attraction field representation for robust line segment detection"

Connected through hyperedge HE-3 (induced by source paper "heterogeneous multi sensor calibration based on graph optimization"):
  with members:
    - "lidar and camera calibration using motion estimated by sensor fusion odometry"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"

Connected through hyperedge HE-4 (induced by source paper "bridging the gap between open source software and vehicle hardware for autonomous driving"):
  with members:
    - "frustum pointnets for 3d object detection from rgb d data"
    - "speed accuracy trade offs for modern convolutional object detectors"

Connected through hyperedge HE-5 (induced by source paper "automatic extrinsic calibration between a camera and a 3d lidar using 3d point and plane correspondences"):
  with members:
    - "automated evaluation of semantic segmentation robustness for autonomous driving"
    - "a novel method for extrinsic calibration of a 2 d laser rangefinder and a camera"

Connected through hyperedge HE-6 (induced by source paper "calibnet self supervised extrinsic calibration using 3d spatial transformer networks"):
  with members:
    - "sbnet sparse blocks network for fast inference"
    - "automatic extrinsic calibration for lidar stereo vehicle sensor setups"
    - "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"

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)