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: "visual inertial navigation a concise review"
  Abstract: As inertial and visual sensors are becoming ubiquitous, visual-inertial navigation systems (VINS) have prevailed in a wide range of applications from mobile augmented reality to aerial navigation to autonomous driving, in part because of the complementary sensing capabilities and the decreasing costs and size of the sensors. In this paper, we survey thoroughly the research efforts taken in this field and strive to provide a concise but complete review of the related work -- which is unfortunately missing in the literature while being greatly demanded by researchers and engineers -- in the hope to accelerate the VINS research and beyond in our society as a whole.

Connected through hyperedge HE-1 (induced by source paper "bidirectional trajectory computation for odometer aided visual inertial slam"):
  with members:
    - "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"
    - "tightly coupled monocular visual odometric slam using wheels and a mems gyroscope"

Connected through hyperedge HE-2 (induced by source paper "tightly coupled fusion of global positional measurements in optimization based visual inertial odometry"):
  with members:
    - "visual inertial odometry of aerial robots"
    - "a general optimization based framework for local odometry estimation with multiple sensors"
    - "vins mono a robust and versatile monocular visual inertial state estimator"
    - "on manifold preintegration for real time visual inertial odometry"

Connected through hyperedge HE-3 (induced by source paper "visual inertial odometry of aerial robots"):
  with members:
    - "online temporal calibration for monocular visual inertial systems"
    - "robust inference for visual inertial sensor fusion"
    - "faster and better a machine learning approach to corner detection"

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)