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: "an incremental sampling based algorithm for stochastic optimal control"
  Abstract: In this paper, we consider a class of continuous-time, continuous-space stochastic optimal control problems. Building upon recent advances in Markov chain approximation methods and sampling-based algorithms for deterministic path planning, we propose a novel algorithm called the incremental Markov Decision Process (iMDP) to compute incrementally control policies that approximate arbitrarily well an optimal policy in terms of the expected cost. The main idea behind the algorithm is to generate a sequence of finite discretizations of the original problem through random sampling of the state space. At each iteration, the discretized problem is a Markov Decision Process that serves as an incrementally refined model of the original problem. We show that with probability one, (i) the sequence of the optimal value functions for each of the discretized problems converges uniformly to the optimal value function of the original stochastic optimal control problem, and (ii) the original optimal value function can be computed efficiently in an incremental manner using asynchronous value iterations. Thus, the proposed algorithm provides an anytime approach to the computation of optimal control policies of the continuous problem. The effectiveness of the proposed approach is demonstrated on motion planning and control problems in cluttered environments in the presence of process noise.

Connected through hyperedge HE-1 (induced by source paper "deep 2fbsdes for systems with control multiplicative noise"):
  with members:
    - "neural network architectures for stochastic control using the nonlinear feynman kac lemma"
    - "deep learning approximation for stochastic control problems"
    - "learning optimal control via forward and backward stochastic differential equations"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "focused model learning and planning for non gaussian continuous state action systems"):
  with members:
    - "more than a million ways to be pushed a high fidelity experimental dataset of planar pushing"
    - "safe exploration in finite markov decision processes with gaussian processes"
    - "bayesian reinforcement learning a survey"
    - "a tutorial on bayesian optimization of expensive cost functions with application to active user modeling and hierarchical reinforcement learning"
    - "settling the polynomial learnability of mixtures of gaussians"

Connected through hyperedge HE-3 (induced by source paper "iterative methods for efficient sampling based optimal motion planning of nonlinear systems"):
  with members:
    - "a martingale approach and time consistent sampling based algorithms for risk management in stochastic optimal control"
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-4 (induced by source paper "planning for optimal feedback control in the volume of free space"):
  with members:
    - "sampling based algorithms for optimal motion planning"

Connected through hyperedge HE-5 (induced by source paper "a martingale approach and time consistent sampling based algorithms for risk management in stochastic optimal control"):
  with members:
    - "mean variance optimization in markov decision processes"
    - "sampling based algorithms for optimal motion planning"

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)