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 odometry of aerial robots"
  Abstract: Visual-Inertial odometry (VIO) is the process of estimating the state (pose and velocity) of an agent (e.g., an aerial robot) by using only the input of one or more cameras plus one or more Inertial Measurement Units (IMUs) attached to it. VIO is the only viable alternative to GPS and lidar-based odometry to achieve accurate state estimation. Since both cameras and IMUs are very cheap, these sensor types are ubiquitous in all today's aerial robots.

Connected through hyperedge HE-1 (induced by source paper "denoising imu gyroscopes with deep learning for open loop attitude estimation"):
  with members:
    - "ronin robust neural inertial navigation in the wild benchmark evaluations and new methods"
    - "rins w robust inertial navigation system on wheels"
    - "ai imu dead reckoning"
    - "selfvio self supervised deep monocular visual inertial odometry and depth estimation"
    - "a mathematical framework for imu error propagation with applications to preintegration"
    - "using inertial sensors for position and orientation estimation"
    - "ionet learning to cure the curse of drift in inertial odometry"
    - "vins mono a robust and versatile monocular visual inertial state estimator"

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 navigation a concise review"
    - "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"

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)