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: "stability certified reinforcement learning a control theoretic perspective"
  Abstract: We investigate the important problem of certifying stability of reinforcement learning policies when interconnected with nonlinear dynamical systems. We show that by regulating the input-output gradients of policies, strong guarantees of robust stability can be obtained based on a proposed semidefinite programming feasibility problem. The method is able to certify a large set of stabilizing controllers by exploiting problem-specific structures; furthermore, we analyze and establish its (non)conservatism. Empirical evaluations on two decentralized control tasks, namely multi-flight formation and power system frequency regulation, demonstrate that the reinforcement learning agents can have high performance within the stability-certified parameter space, and also exhibit stable learning behaviors in the long run.

Connected through hyperedge HE-1 (induced by source paper "robust design of deep neural networks against adversarial attacks based on lyapunov theory"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "certified adversarial robustness via randomized smoothing"
    - "the coupling effect of lipschitz regularization in deep neural networks"
    - "connecting lyapunov control theory to adversarial attacks"
    - "the singular values of convolutional layers"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "l2 nonexpansive neural networks"
    - "adversarial vulnerability for any classifier"

Connected through hyperedge HE-2 (induced by source paper "efficient and accurate estimation of lipschitz constants for deep neural networks"):
  with members:
    - "provable certificates for adversarial examples fitting a ball in the union of polytopes"
    - "scaling provable adversarial defenses"
    - "on lipschitz bounds of general convolutional neural networks"
    - "limitations of the lipschitz constant as a defense against adversarial examples"
    - "lipschitz margin training scalable certification of perturbation invariance for deep neural networks"
    - "l2 nonexpansive neural networks"
    - "semidefinite relaxations for certifying robustness to adversarial examples"
    - "reachability analysis of deep neural networks with provable guarantees"

Connected through hyperedge HE-3 (induced by source paper "verification of neural network control policy under persistent adversarial perturbation"):
  with members:
    - "efficient and accurate estimation of lipschitz constants for deep neural networks"
    - "fast neural network verification via shadow prices"
    - "the lyapunov neural network adaptive stability certification for safe learning of dynamical systems"
    - "verisig verifying safety properties of hybrid systems with neural network controllers"
    - "a lyapunov based approach to safe reinforcement learning"
    - "reachability analysis and safety verification for neural network control systems"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial attacks on neural network policies"

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.SY(Systems and Control)