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: "reachability analysis and safety verification for neural network control systems"
  Abstract: Autonomous cyber-physical systems (CPS) rely on the correct operation of numerous components, with state-of-the-art methods relying on machine learning (ML) and artificial intelligence (AI) components in various stages of sensing and control. This paper develops methods for estimating the reachable set and verifying safety properties of dynamical systems under control of neural network-based controllers that may be implemented in embedded software. The neural network controllers we consider are feedforward neural networks called multilayer perceptrons (MLP) with general activation functions. As such feedforward networks are memoryless, they may be abstractly represented as mathematical functions, and the reachability analysis of the network amounts to range (image) estimation of this function provided a set of inputs. By discretizing the input set of the MLP into a finite number of hyper-rectangular cells, our approach develops a linear programming (LP) based algorithm for over-approximating the output set of the MLP with its input set as a union of hyper-rectangular cells. Combining the over-approximation for the output set of an MLP based controller and reachable set computation routines for ordinary difference/differential equation (ODE) models, an algorithm is developed to estimate the reachable set of the closed-loop system. Finally, safety verification for neural network control systems can be performed by checking the existence of intersections between the estimated reachable set and unsafe regions. The approach is implemented in a computational software prototype and evaluated on numerical examples.

Connected through hyperedge HE-1 (induced by source paper "validation of image based neural network controllers through adaptive stress testing"):
  with members:
    - "algorithms for verifying deep neural networks"
    - "efficient autonomy validation in simulation with adaptive stress testing"
    - "verisig verifying safety properties of hybrid systems with neural network controllers"
    - "efficient formal safety analysis of neural networks"
    - "an approach to reachability analysis for feed forward relu neural networks"
    - "towards proving the adversarial robustness of deep neural networks"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "learning quadrotor dynamics using neural network for flight control"

Connected through hyperedge HE-2 (induced by source paper "a roadmap toward the resilient internet of things for cyber physical systems"):
  with members:
    - "model based verification and validation of an autonomous vehicle system"
    - "deepmarks a digital fingerprinting framework for deep neural networks"
    - "mlcapsule guarded offline deployment of machine learning as a service"
    - "introduction of a hybrid monitor to cyber physical systems"
    - "verification for machine learning autonomy and neural networks survey"
    - "re purposing compact neuronal circuit policies to govern reinforcement learning tasks"
    - "efficient deep learning on multi source private data"
    - "deepsigns a generic watermarking framework for ip protection of deep learning models"

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"
    - "stability certified reinforcement learning a control theoretic perspective"
    - "verisig verifying safety properties of hybrid systems with neural network controllers"
    - "a lyapunov based approach to safe reinforcement learning"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial attacks on neural network policies"

Connected through hyperedge HE-4 (induced by source paper "a reachability method for verifying dynamical systems with deep neural network controllers"):
  with members:
    - "algorithms for verifying deep neural networks"
    - "juliareach a toolbox for set based reachability"
    - "formal security analysis of neural networks using symbolic intervals"
    - "verisig verifying safety properties of hybrid systems with neural network controllers"
    - "reachable set estimation and verification for neural network models of nonlinear dynamic systems"
    - "reachable set computation and safety verification for neural networks with relu activations"
    - "evaluating robustness of neural networks with mixed integer programming"
    - "an approach to reachability analysis for feed forward relu neural networks"

Connected through hyperedge HE-5 (induced by source paper "gray box adversarial testing for control systems with machine learning component"):
  with members:
    - "counterexample guided data augmentation"
    - "simulation based adversarial test generation for autonomous vehicles with machine learning components"
    - "output range analysis for deep neural networks"
    - "systematic testing of convolutional neural networks for autonomous driving"
    - "compositional falsification of cyber physical systems with machine learning components"
    - "towards verified artificial intelligence"
    - "safety verification of deep neural networks"
    - "towards evaluating the robustness of neural networks"

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)