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: "exploring deep and recurrent architectures for optimal control"
  Abstract: Sophisticated multilayer neural networks have achieved state of the art results on multiple supervised tasks. However, successful applications of such multilayer networks to control have so far been limited largely to the perception portion of the control pipeline. In this paper, we explore the application of deep and recurrent neural networks to a continuous, high-dimensional locomotion task, where the network is used to represent a control policy that maps the state of the system (represented by joint angles) directly to the torques at each joint. By using a recent reinforcement learning algorithm called guided policy search, we can successfully train neural network controllers with thousands of parameters, allowing us to compare a variety of architectures. We discuss the differences between the locomotion control task and previous supervised perception tasks, present experimental results comparing various architectures, and discuss future directions in the application of techniques from deep learning to the problem of optimal control.

Connected through hyperedge HE-1 (induced by source paper "learning a family of optimal state feedback controllers"):
  with members:
    - "learning the optimal state feedback via supervised imitation learning"
    - "machine learning and evolutionary techniques in interplanetary trajectory design"
    - "adding neural network controllers to behavior trees without destroying performance guarantees"
    - "whole body nonlinear model predictive control through contacts for quadrupeds"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"
    - "a fast integrated planning and control framework for autonomous driving via imitation learning"
    - "real time optimal control via deep neural networks study on landing problems"
    - "plato policy learning using adaptive trajectory optimization"

Connected through hyperedge HE-2 (induced by source paper "adding neural network controllers to behavior trees without destroying performance guarantees"):
  with members:
    - "machine learning and evolutionary techniques in interplanetary trajectory design"
    - "behavior trees in robotics and ai an introduction"
    - "a lyapunov based approach to safe reinforcement learning"
    - "deep learning for video game playing"
    - "simulation results on selector adaptation in behavior trees"
    - "costar instructing collaborative robots with behavior trees and vision"
    - "learning of behavior trees for autonomous agents"
    - "a neural algorithm of artistic style"

Connected through hyperedge HE-3 (induced by source paper "on the stability analysis of deep neural network representations of an optimal state feedback"):
  with members:
    - "learning the optimal state feedback via supervised imitation learning"
    - "generative adversarial imitation learning"
    - "real time optimal control via deep neural networks study on landing problems"
    - "learning deep control policies for autonomous aerial vehicles with mpc guided policy search"

Connected through hyperedge HE-4 (induced by source paper "memory augmented control networks"):
  with members:
    - "learning visual servoing with deep features and fitted q iteration"
    - "neural map structured memory for deep reinforcement learning"
    - "neural slam learning to explore with external memory"
    - "neural network memory architectures for autonomous robot navigation"
    - "cognitive mapping and planning for visual navigation"
    - "learning transferable policies for monocular reactive mav control"
    - "asynchronous methods for deep reinforcement learning"
    - "value iteration networks"

Connected through hyperedge HE-5 (induced by source paper "learning contact rich manipulation skills with guided policy search"):
  with members:
    - "a reduction of imitation learning and structured prediction to no regret online learning"

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.LG(Machine Learning)