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 monocular visual odometric slam using wheels and a mems gyroscope"
  Abstract: In this paper, we present a novel tightly-coupled probabilistic monocular visual-odometric Simultaneous Localization and Mapping algorithm using wheels and a MEMS gyroscope, which can provide accurate, robust and long-term localization for the ground robot moving on a plane. Firstly, we present an odometer preintegration theory that integrates the wheel encoder measurements and gyroscope measurements to a local frame. The preintegration theory properly addresses the manifold structure of the rotation group SO(3) and carefully deals with uncertainty propagation and bias correction. Then the novel odometer error term is formulated using the odometer preintegration model and it is tightly integrated into the visual optimization framework. Furthermore, we introduce a complete tracking framework to provide different strategies for motion tracking when (1) both measurements are available, (2) visual measurements are not available, and (3) wheel encoder experiences slippage, which leads the system to be accurate and robust. Finally, the proposed algorithm is evaluated by performing extensive experiments, the experimental results demonstrate the superiority of the proposed system.

Connected through hyperedge HE-1 (induced by source paper "bidirectional trajectory computation for odometer aided visual inertial slam"):
  with members:
    - "visual inertial navigation a concise review"
    - "a general optimization based framework for local odometry estimation with multiple sensors"
    - "visual inertial localization for skid steering robots with kinematic constraints"
    - "localization for ground robots on manifold representation integration re parameterization and optimization"
    - "vins mono a robust and versatile monocular visual inertial state estimator"

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"
    - "robocentric visual inertial odometry"
    - "ionet learning to cure the curse of drift in inertial odometry"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "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 "visual inertial localization for skid steering robots with kinematic constraints"):
  with members:
    - "past present and future of simultaneous localization and mapping toward the robust perception age"

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)