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: "neural network architectures for stochastic control using the nonlinear feynman kac lemma"
  Abstract: In this paper we propose a new methodology for decision-making under uncertainty using recent advancements in the areas of nonlinear stochastic optimal control theory, applied mathematics and machine learning. Our work is grounded on the nonlinear Feynman-Kac lemma and the fundamental connection between backward nonlinear partial differential equations and forward-backward stochastic differential equations. Using these connections and results from our prior work on importance sampling for forward-backward stochastic differential equations, we develop a control framework that is scalable and applicable to general classes of stochastic systems and decision-making problem formulations in robotics and autonomy. Two architectures for stochastic control are proposed that consist of feed-forward and recurrent neural networks. The performance and scalability of the aforementioned algorithms is investigated in two stochastic optimal control problem formulations including the unconstrained L2 and control-constrained case, and three systems in simulation. We conclude with a discussion on the implications of the proposed algorithms to robotics and autonomous systems.

Connected through hyperedge HE-1 (induced by source paper "deep 2fbsdes for systems with control multiplicative noise"):
  with members:
    - "deep learning approximation for stochastic control problems"
    - "learning optimal control via forward and backward stochastic differential equations"
    - "adam a method for stochastic optimization"
    - "an incremental sampling based algorithm for stochastic optimal control"

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)