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: "tightly coupled 3d lidar inertial odometry and mapping"
  Abstract: Ego-motion estimation is a fundamental requirement for most mobile robotic applications. By sensor fusion, we can compensate the deficiencies of stand-alone sensors and provide more reliable estimations. We introduce a tightly coupled lidar-IMU fusion method in this paper. By jointly minimizing the cost derived from lidar and IMU measurements, the lidar-IMU odometry (LIO) can perform well with acceptable drift after long-term experiment, even in challenging cases where the lidar measurements can be degraded. Besides, to obtain more reliable estimations of the lidar poses, a rotation-constrained refinement algorithm (LIO-mapping) is proposed to further align the lidar poses with the global map. The experiment results demonstrate that the proposed method can estimate the poses of the sensor pair at the IMU update rate with high precision, even under fast motion conditions or with insufficient features.

Connected through hyperedge HE-1 (induced by source paper "omnislam omnidirectional localization and dense mapping for wide baseline multi camera systems"):
  with members:
    - "omnimvs end to end learning for omnidirectional stereo matching"
    - "ga net guided aggregation net for end to end stereo matching"
    - "rovo robust omnidirectional visual odometry for wide baseline wide fov camera systems"
    - "sweepnet wide baseline omnidirectional depth estimation"
    - "occlusions motion and depth boundaries with a generic network for disparity optical flow or scene flow estimation"
    - "real time dense mapping for self driving vehicles using fisheye cameras"
    - "omnidirectional dso direct sparse odometry with fisheye cameras"
    - "pyramid stereo matching network"

Connected through hyperedge HE-2 (induced by source paper "monocular direct sparse localization in a prior 3d surfel map"):
  with members:
    - "laser map aided visual inertial localization in changing environment"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "carla an open urban driving simulator"
    - "direct sparse odometry"
    - "orb slam a versatile and accurate monocular slam system"

Connected through hyperedge HE-3 (induced by source paper "active perception with a monocular camera for multiscopic vision"):
  with members:
    - "reinforcement learning in topology based representation for human body movement with whole arm manipulation"
    - "revisiting active perception"
    - "stereo matching by training a convolutional neural network to compare image patches"

Connected through hyperedge HE-4 (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"
    - "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-5 (induced by source paper "a lightweight and accurate localization algorithm using multiple inertial measurement units"):
  with members:
    - "localization for ground robots on manifold representation integration re parameterization and optimization"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "robocentric visual inertial odometry"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "visual inertial self calibration on informative motion segments"
    - "on manifold preintegration for real time visual inertial odometry"

Connected through hyperedge HE-6 (induced by source paper "r lins a robocentric lidar inertial state estimator for robust and efficient navigation"):
  with members:
    - "direct sparse visual inertial odometry using dynamic marginalization"
    - "robocentric visual inertial odometry"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "elastic lidar fusion dense map centric continuous time slam"
    - "quaternion kinematics for the error state kalman filter"

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)