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: "verifying controllers against adversarial examples with bayesian optimization"
  Abstract: Recent successes in reinforcement learning have lead to the development of complex controllers for realworld robots. As these robots are deployed in safety-critical applications and interact with humans, it becomes critical to ensure safety in order to avoid causing harm. A first step in this direction is to test the controllers in simulation. To be able to do this, we need to capture what we mean by safety and then efficiently search the space of all behaviors to see if they are safe. In this paper, we present an active-testing framework based on Bayesian Optimization. We specify safety constraints using logic and exploit structure in the problem in order to test the system for adversarial counter examples that violate the safety specifications. These specifications are defined as complex boolean combinations of smooth functions on the trajectories and, unlike reward functions in reinforcement learning, are expressive and impose hard constraints on the system. In our framework, we exploit regularity assumptions on individual functions in form of a Gaussian Process (GP) prior. We combine these into a coherent optimization framework using problem structure. The resulting algorithm is able to provably verify complex safety specifications or alternatively find counter examples. Experimental results show that the proposed method is able to find adversarial examples quickly.

Connected through hyperedge HE-1 (induced by source paper "composing diverse policies for temporally extended tasks"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "task agnostic dynamics priors for deep reinforcement learning"
    - "using causal analysis to learn specifications from task demonstrations"
    - "tossingbot learning to throw arbitrary objects with residual physics"
    - "deepmdp learning continuous latent space models for representation learning"
    - "from explanation to synthesis compositional program induction for learning from demonstration"
    - "reinforcement learning on variable impedance controller for high precision robotic assembly"
    - "learning safe policies with expert guidance"

Connected through hyperedge HE-2 (induced by source paper "adversarial policies attacking deep reinforcement learning"):
  with members:
    - "open ended learning in symmetric zero sum games"
    - "on the geometry of adversarial examples"
    - "feature denoising for improving adversarial robustness"
    - "deep counterfactual regret minimization"
    - "are adversarial examples inevitable"
    - "verifiable reinforcement learning via policy extraction"
    - "motivating the rules of the game for adversarial example research"
    - "adversarial attacks on neural network policies"

Connected through hyperedge HE-3 (induced by source paper "verifiably safe off model reinforcement learning"):
  with members:
    - "logically correct reinforcement learning"
    - "shielded decision making in mdps"
    - "falsification of cyber physical systems using deep reinforcement learning"
    - "a complete uniform substitution calculus for differential dynamic logic"
    - "planning fast and slow a framework for adaptive real time safe trajectory planning"
    - "fastrack a modular framework for fast and guaranteed safe motion planning"
    - "constrained policy optimization"
    - "safe reinforcement learning via shielding"

Connected through hyperedge HE-4 (induced by source paper "safe exploration of nonlinear dynamical systems a predictive safety filter for reinforcement learning"):
  with members:
    - "linear model predictive safety certification for learning based control"
    - "learning to adapt meta learning for model based control"
    - "cautious model predictive control using gaussian process regression"
    - "cautious nmpc with gaussian process dynamics for autonomous miniature race cars"
    - "data efficient reinforcement learning with probabilistic model predictive control"
    - "a general safety framework for learning based control in uncertain robotic systems"
    - "constrained policy optimization"
    - "scalable synthesis of safety certificates from data with application to learning based control"

Connected through hyperedge HE-5 (induced by source paper "adversarial learning based on line anomaly monitoring for assured autonomy"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "stochastic variational video prediction"
    - "reluplex an efficient smt solver for verifying deep neural networks"
    - "deeptest automated testing of deep neural network driven autonomous cars"
    - "deep visual foresight for planning robot motion"
    - "end to end learning for self driving cars"
    - "improved techniques for training gans"
    - "deep predictive coding networks for video prediction and unsupervised learning"

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)