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: "adversarial reinforcement learning framework for benchmarking collision avoidance mechanisms in autonomous vehicles"
  Abstract: With the rapidly growing interest in autonomous navigation, the body of research on motion planning and collision avoidance techniques has enjoyed an accelerating rate of novel proposals and developments. However, the complexity of new techniques and their safety requirements render the bulk of current benchmarking frameworks inadequate, thus leaving the need for efficient comparison techniques unanswered. This work proposes a novel framework based on deep reinforcement learning for benchmarking the behavior of collision avoidance mechanisms under the worst-case scenario of dealing with an optimal adversarial agent, trained to drive the system into unsafe states. We describe the architecture and flow of this framework as a benchmarking solution, and demonstrate its efficacy via a practical case study of comparing the reliability of two collision avoidance mechanisms in response to intentional collision attempts.

Connected through hyperedge HE-1 (induced by source paper "challenges and countermeasures for adversarial attacks on deep reinforcement learning"):
  with members:
    - "adversarial policies attacking deep reinforcement learning"
    - "distilling policy distillation"
    - "sequential triggers for watermarking of deep reinforcement learning policies"
    - "deceptive reinforcement learning under adversarial manipulations on cost signals"
    - "adversarial exploitation of policy imitation"
    - "wasserstein robust reinforcement learning"
    - "rl based method for benchmarking the adversarial resilience and robustness of deep reinforcement learning policies"
    - "analysis and improvement of adversarial training in dqn agents with adversarially guided exploration age"

Connected through hyperedge HE-2 (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"
    - "adaptive stress testing for autonomous vehicles"
    - "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"
    - "reward estimation for variance reduction in deep reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "manipulating reinforcement learning poisoning attacks on cost signals"):
  with members:
    - "deceptive reinforcement learning under adversarial manipulations on cost signals"
    - "policy poisoning in batch reinforcement learning and control"
    - "adaptive honeypot engagement through reinforcement learning of semi markov decision processes"
    - "reinforcement learning with perturbed rewards"
    - "the faults in our pi stars security issues and open challenges in deep reinforcement learning"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "reinforcement learning with a corrupted reward channel"

Connected through hyperedge HE-4 (induced by source paper "deceptive reinforcement learning under adversarial manipulations on cost signals"):
  with members:
    - "game theoretic analysis of cyber deception evidence based strategies and dynamic risk mitigation"
    - "reinforcement learning with perturbed rewards"
    - "modeling and analysis of leaky deception using signaling games with evidence"
    - "istrict an interdependent strategic trust mechanism for the cloud enabled internet of controlled things"
    - "the faults in our pi stars security issues and open challenges in deep reinforcement learning"
    - "optimal timing in dynamic and robust attacker engagement during advanced persistent threats"
    - "reinforcement learning with a corrupted reward channel"
    - "a game theoretic taxonomy and survey of defensive deception for cybersecurity and privacy"

Connected through hyperedge HE-5 (induced by source paper "the faults in our pi stars security issues and open challenges in deep reinforcement learning"):
  with members:
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "mitigation of policy manipulation attacks on deep q networks with parameter space noise"
    - "sequential attacks on agents for long term adversarial goals"
    - "sok security and privacy in machine learning"
    - "a psychopathological approach to safety engineering in ai and agi"
    - "online robust policy learning in the presence of unknown adversaries"
    - "reinforcement learning for autonomous defence in software defined networking"
    - "ai safety gridworlds"

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)