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: "probabilistic occupancy function and sets using forward stochastic reachability for rigid body dynamic obstacles"
  Abstract: Given multiple rigid-body obstacles that have known stochastic dynamics and a desired probabilistic safety guarantee, we discuss theory and algorithms for the computation of the associated probability-weighted keep-out sets. Using these sets, dynamically-feasible robot trajectories that have a probability of collision below a desired threshold may be generated using existing motion planners that can handle non-stochastic dynamic obstacles. We define a probabilistic occupancy function that provides the probabilility of collision of the robot with an obstacle when the robot is at a given state and time of interest. The desired keep-out sets are the appropriate super-level sets of the occupancy function. We compute this function using forward stochastic reachability which characterizes the stochasticity of the state of the obstacle at a future time of interest given its initial state. We focus on discrete-time Markovian switched systems with affine parameter-varying stochastic subsystems (DMSP), which includes Markov jump affine systems and discrete-time affine parameter-varying stochastic systems (DPV), and show that forward stochastic reachability for this model can be performed, grid-free and recursion-free, using Fourier transforms and computational geometry. We also provide sufficient conditions that ensure convex and compact keep-out sets for DPV obstacle dynamics. Using these results, we propose two computationally efficient algorithms that provide approximations of these sets --- a tight polytopic representation using projections, and an overapproximation using Minkowski sum. For DMSP obstacle dynamics, we compute a union of convex and compact sets that covers the potentially non-convex keep-out set. Numerical simulations show the efficacy of the proposed algorithms for a modified version of the classical unicycle dynamics, modeled as a DMSP.

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.SY(Systems and Control)