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: "verisig verifying safety properties of hybrid systems with neural network controllers"
  Abstract: This paper presents Verisig, a hybrid system approach to verifying safety properties of closed-loop systems using neural networks as controllers. Although techniques exist for verifying input/output properties of the neural network itself, these methods cannot be used to verify properties of the closed-loop system (since they work with piecewise-linear constraints that do not capture non-linear plant dynamics). To overcome this challenge, we focus on sigmoid-based networks and exploit the fact that the sigmoid is the solution to a quadratic differential equation, which allows us to transform the neural network into an equivalent hybrid system. By composing the network's hybrid system with the plant's, we transform the problem into a hybrid system verification problem which can be solved using state-of-the-art reachability tools. We show that reachability is decidable for networks with one hidden layer and decidable for general networks if Schanuel's conjecture is true. We evaluate the applicability and scalability of Verisig in two case studies, one from reinforcement learning and one in which the neural network is used to approximate a model predictive controller.

Connected through hyperedge HE-1 (induced by source paper "resilient cyberphysical systems and their application drivers a technology roadmap"):
  with members:
    - "traditional and heavy tailed self regularization in neural network models"
    - "numerical verification of affine systems with up to a billion dimensions"
    - "juliareach a toolbox for set based reachability"
    - "heavy tailed universality predicts trends in test accuracies for very large pre trained deep neural networks"
    - "netadapt platform aware neural network adaptation for mobile applications"
    - "formal security analysis of neural networks using symbolic intervals"
    - "a dual approach to scalable verification of deep networks"
    - "semidefinite relaxations for certifying robustness to adversarial examples"

Connected through hyperedge HE-2 (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"
    - "reachability analysis and safety verification for neural network control systems"
    - "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-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"
    - "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"

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"
    - "reachability analysis and safety verification for neural network control systems"
    - "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 "safe reinforcement learning via online shielding"):
  with members:
    - "a lyapunov based approach to safe reinforcement learning"
    - "programmatically interpretable reinforcement learning"
    - "verifiable reinforcement learning via policy extraction"
    - "constrained policy optimization"
    - "safe reinforcement learning via shielding"
    - "safe exploration in finite markov decision processes with gaussian processes"
    - "concrete problems in ai safety"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-6 (induced by source paper "learning a safety verifiable adaptive cruise controller from human driving data"):
  with members:
    - "reachable set estimation and verification for neural network models of nonlinear dynamic systems"
    - "deepxplore automated whitebox testing of deep learning systems"
    - "deeptest automated testing of deep neural network driven autonomous cars"
    - "end to end learning for self driving cars"

Connected through hyperedge HE-7 (induced by source paper "testing deep neural networks"):
  with members:
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "a formalization of robustness for deep neural networks"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "formal security analysis of neural networks using symbolic intervals"
    - "formal verification of neural network controlled autonomous systems"
    - "symbolic execution for deep neural networks"
    - "identifying implementation bugs in machine learning based image classifiers using metamorphic testing"
    - "deepmutation mutation testing of deep learning systems"

Connected through hyperedge HE-8 (induced by source paper "formal verification of neural network controlled autonomous systems"):
  with members:
    - "adversarial sample detection for deep neural network through model mutation testing"
    - "deepgauge comprehensive and multi granularity testing criteria for gauging the robustness of deep learning systems"
    - "automated verification of neural networks advances challenges and perspectives"
    - "robust deep reinforcement learning for security and safety in autonomous vehicle systems"
    - "agi safety literature review"
    - "deepgauge multi granularity testing criteria for deep learning systems"
    - "deepmutation mutation testing of deep learning systems"
    - "concolic testing for deep 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)