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: "probe predictive robust estimation for visual inertial navigation"
  Abstract: Navigation in unknown, chaotic environments continues to present a significant challenge for the robotics community. Lighting changes, self-similar textures, motion blur, and moving objects are all considerable stumbling blocks for state-of-the-art vision-based navigation algorithms. In this paper we present a novel technique for improving localization accuracy within a visual-inertial navigation system (VINS). We make use of training data to learn a model for the quality of visual features with respect to localization error in a given environment. This model maps each visual observation from a predefined prediction space of visual-inertial predictors onto a scalar weight, which is then used to scale the observation covariance matrix. In this way, our model can adjust the influence of each observation according to its quality. We discuss our choice of predictors and report substantial reductions in localization error on 4 km of data from the KITTI dataset, as well as on experimental datasets consisting of 700 m of indoor and outdoor driving on a small ground rover equipped with a Skybotix VI-Sensor.

Connected through hyperedge HE-1 (induced by source paper "dpc net deep pose correction for visual localization"):
  with members:
    - "how to train a cat learning canonical appearance transformations for direct visual localization under illumination change"
    - "geometric loss functions for camera pose regression with deep learning"
    - "vinet visual inertial odometry as a sequence to sequence learning problem"
    - "topometric localization with deep learning"
    - "probe gk predictive robust estimation using generalized kernels"
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "deep neural networks for improved impromptu trajectory tracking of quadrotors"
    - "benchmarking deep reinforcement learning for continuous control"

Connected through hyperedge HE-2 (induced by source paper "attention and anticipation in fast visual inertial navigation"):
  with members:
    - "past present and future of simultaneous localization and mapping toward the robust perception age"
    - "on manifold preintegration theory for fast and accurate visual inertial navigation"
    - "you only look once unified real time object detection"
    - "orb slam a versatile and accurate monocular slam system"
    - "high performance and tunable stereo reconstruction"
    - "recurrent models of visual attention"

Connected through hyperedge HE-3 (induced by source paper "probe gk predictive robust estimation using generalized kernels"):
  with members:
    - "robust inference for visual inertial sensor fusion"

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)