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: "whatever does not kill deep reinforcement learning makes it stronger"
  Abstract: Recent developments have established the vulnerability of deep Reinforcement Learning (RL) to policy manipulation attacks via adversarial perturbations. In this paper, we investigate the robustness and resilience of deep RL to training-time and test-time attacks. Through experimental results, we demonstrate that under noncontiguous training-time attacks, Deep Q-Network (DQN) agents can recover and adapt to the adversarial conditions by reactively adjusting the policy. Our results also show that policies learned under adversarial perturbations are more robust to test-time attacks. Furthermore, we compare the performance of $\epsilon$-greedy and parameter-space noise exploration methods in terms of robustness and resilience against adversarial perturbations.

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 "robust deep reinforcement learning against adversarial perturbations on observations"):
  with members:
    - "automatic perturbation analysis on general computational graphs"
    - "certified adversarial robustness for deep reinforcement learning"
    - "online robustness training for deep reinforcement learning"
    - "action robust reinforcement learning and applications in continuous control"
    - "towards stable and efficient training of verifiably robust neural networks"
    - "a convex relaxation barrier to tight robustness verification of neural networks"
    - "robust reinforcement learning for continuous control with model misspecification"
    - "scaling provable adversarial defenses"

Connected through hyperedge HE-3 (induced by source paper "online robustness training for deep reinforcement learning"):
  with members:
    - "certified adversarial robustness for deep reinforcement learning"
    - "a provable defense for deep residual networks"
    - "scaling provable adversarial defenses"
    - "adversary a3c for robust reinforcement learning"
    - "robust deep reinforcement learning for security and safety in autonomous vehicle systems"
    - "training verified learners with learned verifiers"
    - "online robust policy learning in the presence of unknown adversaries"
    - "certified defenses against adversarial examples"

Connected through hyperedge HE-4 (induced by source paper "learning to cope with adversarial attacks"):
  with members:
    - "flow shape design for microfluidic devices using deep reinforcement learning"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "sequential attacks on agents for long term adversarial goals"
    - "online robust policy learning in the presence of unknown adversaries"
    - "adversarial attacks on neural network policies"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "meta learning shared hierarchies"
    - "detecting adversarial attacks on neural network policies with visual foresight"

Connected through hyperedge HE-5 (induced by source paper "analysis and improvement of adversarial training in dqn agents with adversarially guided exploration age"):
  with members:
    - "a dissection of overfitting and generalization in continuous reinforcement learning"
    - "the faults in our pi stars security issues and open challenges in deep reinforcement learning"
    - "robust deep reinforcement learning with adversarial attacks"
    - "robust adversarial reinforcement learning"
    - "boltzmann exploration done right"
    - "towards generalization and simplicity in continuous control"
    - "noisy networks for exploration"

Connected through hyperedge HE-6 (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"
    - "adversarial reinforcement learning framework for benchmarking collision avoidance mechanisms in autonomous vehicles"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "mitigation of policy manipulation attacks on deep q networks with parameter space noise"):
  with members:
    - "semisupervised deep reinforcement learning in support of iot and smart city services"
    - "adversarial attacks on neural network policies"
    - "parameter space noise for exploration"
    - "robust deep reinforcement learning with adversarial attacks"
    - "detecting adversarial attacks on neural network policies with visual foresight"
    - "vulnerability of deep reinforcement learning to policy induction attacks"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "practical black box attacks against machine learning"

Connected through hyperedge HE-8 (induced by source paper "coordination driven learning in multi agent problem spaces"):
  with members:
    - "measuring collaborative emergent behavior in multi agent reinforcement learning"
    - "adversarial attacks on neural network policies"
    - "counterfactual multi agent policy gradients"
    - "robust deep reinforcement learning with adversarial attacks"
    - "deep decentralized multi task multi agent reinforcement learning under partial observability"
    - "a survey of learning in multiagent environments dealing with non stationarity"

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.AI(Artificial Intelligence)