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: "sparse gaussian processes for continuous time trajectory estimation on matrix lie groups"
  Abstract: Continuous-time trajectory representations are a powerful tool that can be used to address several issues in many practical simultaneous localization and mapping (SLAM) scenarios, like continuously collected measurements distorted by robot motion, or during with asynchronous sensor measurements. Sparse Gaussian processes (GP) allow for a probabilistic non-parametric trajectory representation that enables fast trajectory estimation by sparse GP regression. However, previous approaches are limited to dealing with vector space representations of state only. In this technical report we extend the work by Barfoot et al. [1] to general matrix Lie groups, by applying constant-velocity prior, and defining locally linear GP. This enables using sparse GP approach in a large space of practical SLAM settings. In this report we give the theory and leave the experimental evaluation in future publications.

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"
    - "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"
    - "batch nonlinear continuous time trajectory estimation as exactly sparse gaussian process regression"

Connected through hyperedge HE-2 (induced by source paper "trajectory optimization for coordinated human robot collaboration"):
  with members:
    - "towards a robust aerial cinematography platform localizing and tracking moving targets in unstructured environments"
    - "riemannian motion policies"
    - "rmpflow a computational graph for automatic motion policy generation"
    - "on the fundamental importance of gauss newton in motion optimization"
    - "newton methods for k order markov constrained motion problems"
    - "continuous inverse optimal control with locally optimal examples"

Connected through hyperedge HE-3 (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"
    - "batch nonlinear continuous time trajectory estimation as exactly sparse gaussian process regression"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-4 (induced by source paper "motion planning in high dimensional spaces"):
  with members:
    - "manipulability maximization using continuous time gaussian processes"
    - "continuous time gaussian process motion planning via probabilistic inference"
    - "functional gradient motion planning in reproducing kernel hilbert spaces"

Connected through hyperedge HE-5 (induced by source paper "multi agent gaussian process motion planning via probabilistic inference"):
  with members:
    - "manipulability maximization using continuous time gaussian processes"
    - "continuous time gaussian process motion planning via probabilistic inference"

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)