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: "batch nonlinear continuous time trajectory estimation as exactly sparse gaussian process regression"
  Abstract: In this paper, we revisit batch state estimation through the lens of Gaussian process (GP) regression. We consider continuous-discrete estimation problems wherein a trajectory is viewed as a one-dimensional GP, with time as the independent variable. Our continuous-time prior can be defined by any nonlinear, time-varying stochastic differential equation driven by white noise; this allows the possibility of smoothing our trajectory estimates using a variety of vehicle dynamics models (e.g., `constant-velocity'). We show that this class of prior results in an inverse kernel matrix (i.e., covariance matrix between all pairs of measurement times) that is exactly sparse (block-tridiagonal) and that this can be exploited to carry out GP regression (and interpolation) very efficiently. When the prior is based on a linear, time-varying stochastic differential equation and the measurement model is also linear, this GP approach is equivalent to classical, discrete-time smoothing (at the measurement times); when a nonlinearity is present, we iterate over the whole trajectory to maximize accuracy. We test the approach experimentally on a simultaneous trajectory estimation and mapping problem using a mobile robot dataset.

Connected through hyperedge HE-1 (induced by source paper "steap simultaneous trajectory estimation and planning"):
  with members:
    - "continuous time gaussian process motion planning via probabilistic inference"
    - "approximately optimal continuous time motion planning and control via probabilistic inference"
    - "sparse gaussian processes for continuous time trajectory estimation on matrix lie groups"
    - "voxblox incremental 3d euclidean signed distance fields for on board mav planning"
    - "non gaussian slap simultaneous localization and planning under non gaussian uncertainty in static and dynamic environments"
    - "4d crop monitoring spatio temporal reconstruction for agriculture"
    - "simultaneous localization and planning for physical mobile robots via enabling dynamic replanning in belief space"

Connected through hyperedge HE-2 (induced by source paper "stochastic optimization for trajectory planning with heteroscedastic gaussian processes"):
  with members:
    - "spatio temporal multisensor calibration based on gaussian processes moving object tracking"
    - "manipulability maximization using continuous time gaussian processes"
    - "continuous time gaussian process motion planning via probabilistic inference"
    - "sparse gaussian processes for continuous time trajectory estimation on matrix lie groups"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-3 (induced by source paper "exactly sparse gaussian variational inference with application to derivative free batch nonlinear state estimation"):
  with members:
    - "continuous time gaussian process motion planning via probabilistic inference"

Connected through hyperedge HE-4 (induced by source paper "spatio temporal multisensor calibration based on gaussian processes moving object tracking"):
  with members:
    - "online slam with any time self calibration and automatic change detection"

Connected through hyperedge HE-5 (induced by source paper "continuous time gaussian process motion planning via probabilistic inference"):
  with members:
    - "steap simultaneous trajectory estimation and planning"
    - "approximately optimal continuous time motion planning and control via probabilistic inference"
    - "functional gradient motion planning in reproducing kernel hilbert spaces"
    - "batch informed trees bit sampling based optimal planning via the heuristically guided search of implicit random geometric graphs"
    - "incremental sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-6 (induced by source paper "past present and future of simultaneous localization and mapping toward the robust perception age"):
  with members:
    - "on manifold preintegration for real time visual inertial odometry"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "unsupervised cnn for single view depth estimation geometry to the rescue"
    - "event based 6 dof camera tracking for high speed applications"
    - "monte carlo localization in hand drawn maps"
    - "on manifold preintegration theory for fast and accurate visual inertial navigation"
    - "monocular slam supported object recognition"
    - "lagrangian duality in 3d slam verification techniques and optimal solutions"

Connected through hyperedge HE-7 (induced by source paper "modeling and interpolation of the ambient magnetic field by gaussian processes"):
  with members:
    - "sequential inference for latent force models"

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)