human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning a family of optimal state feedback controllers"):
  Abstract: Solving optimal control problems is well known to be very computationally demanding. In this paper we show how a combination of Pontryagin's minimum principle and machine learning can be used to learn optimal feedback controllers for a parametric cost function. This enables an unmanned system with limited computational resources to run optimal feedback controllers, and furthermore change the objective being optimised on the fly in response to external events. Thus, a time optimal control policy can be changed to a fuel optimal one, in the event of e.g., fuel leakage. The proposed approach is illustrated on both a standard inverted pendulum swing-up problem and a more complex interplanetary spacecraft orbital transfer.

Includes members:
  Member "learning the optimal state feedback via supervised imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "real time optimal guidance and control for interplanetary transfers using deep networks")
  Member "machine learning and evolutionary techniques in interplanetary trajectory design":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning the optimal state feedback via supervised imitation learning")
  Member "adding neural network controllers to behavior trees without destroying performance guarantees":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "analysis and exploitation of synchronized parallel executions in behavior trees")
  Member "whole body nonlinear model predictive control through contacts for quadrupeds":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "guided constrained policy optimization for dynamic quadrupedal robot locomotion")
  Member "neural network dynamics for model based deep reinforcement learning with model free fine tuning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning")
  Member "a fast integrated planning and control framework for autonomous driving via imitation learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient motion planning for automated lane change based on imitation learning and mixed integer optimization")
  Member "real time optimal control via deep neural networks study on landing problems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "real time optimal guidance and control for interplanetary transfers using deep networks")
  Member "plato policy learning using adaptive trajectory optimization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adapt zero shot adaptive policy transfer for stochastic dynamical systems")

Question: Please tell me which class the source paper 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)