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: "robocentric visual inertial odometry"
  Abstract: In this paper, we propose a novel robocentric formulation of the visual-inertial navigation system (VINS) within a sliding-window filtering framework and design an efficient, lightweight, robocentric visual-inertial odometry (R-VIO) algorithm for consistent motion tracking even in challenging environments using only a monocular camera and a 6-axis IMU. The key idea is to deliberately reformulate the VINS with respect to a moving local frame, rather than a fixed global frame of reference as in the standard world-centric VINS, in order to obtain relative motion estimates of higher accuracy for updating global poses. As an immediate advantage of this robocentric formulation, the proposed R-VIO can start from an arbitrary pose, without the need to align the initial orientation with the global gravitational direction. More importantly, we analytically show that the linearized robocentric VINS does not undergo the observability mismatch issue as in the standard world-centric counterpart which was identified in the literature as the main cause of estimation inconsistency. Additionally, we investigate in-depth the special motions that degrade the performance in the world-centric formulation and show that such degenerate cases can be easily compensated in the proposed robocentric formulation, without resorting to additional sensors as in the world-centric formulation, thus leading to better robustness. The proposed R-VIO algorithm has been extensively tested through both Monte Carlo simulations and real-world experiments with different sensor platforms navigating in different environments, and shown to achieve better (or competitive at least) performance than the state-of-the-art VINS, in terms of consistency, accuracy and efficiency.

Connected through hyperedge HE-1 (induced by source paper "visual inertial navigation a concise review"):
  with members:
    - "observability aware self calibration of visual and inertial sensors for ego motion estimation"
    - "closed form preintegration methods for graph based visual inertial navigation"
    - "continuous time visual inertial odometry for event cameras"
    - "visual inertial object detection and mapping"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "stereo vision based semantic 3d object and ego motion tracking for autonomous driving"
    - "closed form solution to cooperative visual inertial structure from motion"
    - "relocalization global optimization and map merging for monocular visual inertial slam"

Connected through hyperedge HE-2 (induced by source paper "localization for ground robots on manifold representation integration re parameterization and optimization"):
  with members:
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "ionet learning to cure the curse of drift in inertial odometry"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "tightly coupled monocular visual odometric slam using wheels and a mems gyroscope"
    - "oxiod the dataset for deep inertial odometry"
    - "visual inertial self calibration on informative motion segments"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"

Connected through hyperedge HE-3 (induced by source paper "an efficient schmidt ekf for 3d visual inertial slam"):
  with members:
    - "relocalization global optimization and map merging for monocular visual inertial slam"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "lightweight unsupervised deep loop closure"
    - "visual inertial monocular slam with map reuse"
    - "consistent map based 3d localization on mobile devices"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "faster and better a machine learning approach to corner detection"

Connected through hyperedge HE-4 (induced by source paper "a lightweight and accurate localization algorithm using multiple inertial measurement units"):
  with members:
    - "tightly coupled 3d lidar inertial odometry and mapping"
    - "localization for ground robots on manifold representation integration re parameterization and optimization"
    - "maplab an open framework for research in visual inertial mapping and localization"
    - "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-5 (induced by source paper "r lins a robocentric lidar inertial state estimator for robust and efficient navigation"):
  with members:
    - "tightly coupled 3d lidar inertial odometry and mapping"
    - "direct sparse visual inertial odometry using dynamic marginalization"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "lic fusion lidar inertial camera odometry"):
  with members:
    - "limo lidar monocular visual odometry"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "probabilistic surfel fusion for dense lidar mapping"
    - "visual inertial monocular slam with map reuse"

Connected through hyperedge HE-7 (induced by source paper "camera pose correction in slam based on bias values of map points"):
  with members:
    - "good feature selection for least squares pose optimization in vo vslam"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "visual inertial monocular slam with map reuse"

Connected through hyperedge HE-8 (induced by source paper "contact aided invariant extended kalman filtering for legged robot state estimation"):
  with members:
    - "rins w robust inertial navigation system on wheels"
    - "closed form preintegration methods for graph based visual inertial navigation"
    - "hybrid contact preintegration for visual inertial contact state estimation using factor graphs"
    - "feedback control of a cassie bipedal robot walking standing and riding a segway"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "invariant smoothing on lie groups"
    - "on manifold preintegration for real time visual inertial odometry"
    - "an ekf slam algorithm with consistency properties"

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)