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 "verification of neural network control policy under persistent adversarial perturbation"):
  Abstract: Deep neural networks are known to be fragile to small adversarial perturbations. This issue becomes more critical when a neural network is interconnected with a physical system in a closed loop. In this paper, we show how to combine recent works on neural network certification tools (which are mainly used in static settings such as image classification) with robust control theory to certify a neural network policy in a control loop. Specifically, we give a sufficient condition and an algorithm to ensure that the closed loop state and control constraints are satisfied when the persistent adversarial perturbation is l-infinity norm bounded. Our method is based on finding a positively invariant set of the closed loop dynamical system, and thus we do not require the differentiability or the continuity of the neural network policy. Along with the verification result, we also develop an effective attack strategy for neural network control systems that outperforms exhaustive Monte-Carlo search significantly. We show that our certification algorithm works well on learned models and achieves 5 times better result than the traditional Lipschitz-based method to certify the robustness of a neural network policy on a cart pole control problem.

Includes members:
  Member "efficient and accurate estimation of lipschitz constants for deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "norm based generalisation bounds for multi class convolutional neural networks")
  Member "fast neural network verification via shadow prices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "branch and bound for piecewise linear neural network verification")
  Member "the lyapunov neural network adaptive stability certification for safe learning of dynamical systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning stable deep dynamics models")
  Member "stability certified reinforcement learning a control theoretic perspective":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "robust design of deep neural networks against adversarial attacks based on lyapunov theory")
  Member "verisig verifying safety properties of hybrid systems with neural network controllers":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "resilient cyberphysical systems and their application drivers a technology roadmap")
  Member "a lyapunov based approach to safe reinforcement learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "constrained upper confidence reinforcement learning")
  Member "reachability analysis and safety verification for neural network control systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "validation of image based neural network controllers through adaptive stress testing")
  Member "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adversarial ranking attack and defense")

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.LG(Machine Learning)