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: "non gaussian slap simultaneous localization and planning under non gaussian uncertainty in static and dynamic environments"
  Abstract: Simultaneous Localization and Planning (SLAP) under process and measurement uncertainties is a challenge. It involves solving a stochastic control problem modeled as a Partially Observed Markov Decision Process (POMDP) in a general framework. For a convex environment, we propose an optimization-based open-loop optimal control problem coupled with receding horizon control strategy to plan for high quality trajectories along which the uncertainty of the state localization is reduced while the system reaches to a goal state with minimum control effort. In a static environment with non-convex state constraints, the optimization is modified by defining barrier functions to obtain collision-free paths while maintaining the previous goals. By initializing the optimization with trajectories in different homotopy classes and comparing the resultant costs, we improve the quality of the solution in the presence of action and measurement uncertainties. In dynamic environments with time-varying constraints such as moving obstacles or banned areas, the approach is extended to find collision-free trajectories. In this paper, the underlying spaces are continuous, and beliefs are non-Gaussian. Without obstacles, the optimization is a globally convex problem, while in the presence of obstacles it becomes locally convex. We demonstrate the performance of the method on different scenarios.

Connected through hyperedge HE-1 (induced by source paper "safe optimal control under parametric uncertainties"):
  with members:
    - "porca modeling and planning for autonomous driving among many pedestrians"
    - "safely probabilistically complete real time planning and exploration in unknown environments"

Connected through hyperedge HE-2 (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"
    - "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-3 (induced by source paper "efficient probabilistic collision detection for non gaussian noise distributions"):
  with members:
    - "probabilistically safe robot planning with confidence based human predictions"
    - "fast and bounded probabilistic collision detection in dynamic environments for high dof trajectory planning"
    - "efficient probabilistic collision detection for non convex shapes"
    - "i planner intention aware motion planning using learning based human motion prediction"

Connected through hyperedge HE-4 (induced by source paper "belief space planning simplified trajectory optimized lqg t lqg extended report"):
  with members:
    - "feedback motion planning under non gaussian uncertainty and non convex state constraints"

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)