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: "adaptive stress testing for autonomous vehicles"
  Abstract: This paper presents a method for testing the decision making systems of autonomous vehicles. Our approach involves perturbing stochastic elements in the vehicle's environment until the vehicle is involved in a collision. Instead of applying direct Monte Carlo sampling to find collision scenarios, we formulate the problem as a Markov decision process and use reinforcement learning algorithms to find the most likely failure scenarios. This paper presents Monte Carlo Tree Search (MCTS) and Deep Reinforcement Learning (DRL) solutions that can scale to large environments. We show that DRL can find more likely failure scenarios than MCTS with fewer calls to the simulator. A simulation scenario involving a vehicle approaching a crosswalk is used to validate the framework. Our proposed approach is very general and can be easily applied to other scenarios given the appropriate models of the vehicle and the environment.

Connected through hyperedge HE-1 (induced by source paper "training adversarial agents to exploit weaknesses in deep control policies"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "a survey of deep learning applications to autonomous vehicle control"
    - "safely entering the deep a review of verification and validation for machine learning and a challenge elicitation in the automotive industry"
    - "training for faster adversarial robustness verification via inducing relu stability"
    - "adversarially robust generalization requires more data"
    - "making sense of vision and touch self supervised learning of multimodal representations for contact rich tasks"
    - "adversarial reinforcement learning framework for benchmarking collision avoidance mechanisms in autonomous vehicles"
    - "reward estimation for variance reduction in deep reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "efficient black box assessment of autonomous vehicle safety"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "verifai a toolkit for the design and analysis of artificial intelligence based systems"
    - "precog prediction conditioned on goals in visual multi agent settings"
    - "fuzzergym a competitive framework for fuzzing and learning"
    - "deep imitative models for flexible inference planning and control"
    - "ai benchmark running deep neural networks on android smartphones"
    - "chauffeurnet learning to drive by imitating the best and synthesizing the worst"
    - "adaptive stress testing finding failure events with reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "efficient autonomy validation in simulation with adaptive stress testing"):
  with members:
    - "scalable end to end autonomous vehicle testing via rare event simulation"
    - "simulation based adversarial test generation for autonomous vehicles with machine learning components"
    - "benchmarking deep reinforcement learning for continuous control"
    - "high dimensional continuous control using generalized advantage estimation"
    - "trust region policy optimization"

Connected through hyperedge HE-4 (induced by source paper "adaptive stress testing with reward augmentation for autonomous vehicle validation"):
  with members:
    - "scalable end to end autonomous vehicle testing via rare event simulation"
    - "simulation based adversarial test generation for autonomous vehicles with machine learning components"
    - "on a formal model of safe and scalable self driving cars"

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.RO(Robotics)