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 "a control lyapunov perspective on episodic learning via projection to state stability"):
  Abstract: The goal of this paper is to understand the impact of learning on control synthesis from a Lyapunov function perspective. In particular, rather than consider uncertainties in the full system dynamics, we employ Control Lyapunov Functions (CLFs) as low-dimensional projections. To understand and characterize the uncertainty that these projected dynamics introduce in the system, we introduce a new notion: Projection to State Stability (PSS). PSS can be viewed as a variant of Input to State Stability defined on projected dynamics, and enables characterizing robustness of a CLF with respect to the data used to learn system uncertainties. We use PSS to bound uncertainty in affine control, and demonstrate that a practical episodic learning approach can use PSS to characterize uncertainty in the CLF for robust control synthesis.

Includes members:
  Member "episodic learning with control lyapunov functions for uncertain robotic systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "beyond no regret competitive control via online optimization with memory")
  Member "stagewise safe bayesian optimization with gaussian processes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "safe linear thompson sampling with side information")
  Member "neural lander stable drone landing control using learned dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to fly via deep model based reinforcement learning")
  Member "differentiable mpc for end to end planning and control":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "objective mismatch in model based reinforcement learning")
  Member "spectral normalization for generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "regret bounds for robust adaptive control of the linear quadratic regulator":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "improper learning for non stochastic control")
  Member "safe controller optimization for quadrotors with gaussian processes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "beyond no regret competitive control via online optimization with memory")
  Member "safe learning of regions of attraction for uncertain nonlinear systems with gaussian processes":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "episodic learning with control lyapunov functions for uncertain robotic 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)