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 gk predictive robust estimation using generalized kernels"
  Abstract: Many algorithms in computer vision and robotics make strong assumptions about uncertainty, and rely on the validity of these assumptions to produce accurate and consistent state estimates. In practice, dynamic environments may degrade sensor performance in predictable ways that cannot be captured with static uncertainty parameters. In this paper, we employ fast nonparametric Bayesian inference techniques to more accurately model sensor uncertainty. By setting a prior on observation uncertainty, we derive a predictive robust estimator, and show how our model can be learned from sample images, both with and without knowledge of the motion used to generate the data. We validate our approach through Monte Carlo simulations, and report significant improvements in localization accuracy relative to a fixed noise model in several settings, including on synthetic data, the KITTI dataset, and our own experimental platform.

Connected through hyperedge HE-1 (induced by source paper "bayesian spatial kernel smoothing for scalabledense semantic mapping"):
  with members:
    - "semantickitti a dataset for semantic scene understanding of lidar sequences"
    - "sampling based incremental information gathering with applications to robotic exploration and environmental monitoring"
    - "rtseg real time semantic segmentation comparative study"
    - "contact aided invariant extended kalman filtering for legged robot state estimation"
    - "semantic 3d occupancy mapping through efficient high order crfs"
    - "gaussian processes semantic map representation"
    - "sparse bayesian inference for dense semantic mapping"
    - "joint 2d 3d semantic data for indoor scene understanding"

Connected through hyperedge HE-2 (induced by source paper "cello 3d estimating the covariance of icp in the real world"):
  with members:
    - "on the covariance of icp based scan matching techniques"

Connected through hyperedge HE-3 (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"
    - "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"
    - "backprop kf learning discriminative deterministic state estimators"

Connected through hyperedge HE-4 (induced by source paper "reducing drift in visual odometry by inferring sun direction using a bayesian convolutional neural network"):
  with members:
    - "find your way by observing the sun and other semantic cues"
    - "direct sparse odometry"
    - "improving the accuracy of stereo visual odometry using visual illumination estimation"
    - "modelling uncertainty in deep learning for camera relocalization"
    - "posenet a convolutional network for real time 6 dof camera relocalization"
    - "going deeper with convolutions"
    - "caffe convolutional architecture for fast feature embedding"

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)