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: "detecting adversarial attacks on neural network policies with visual foresight"
  Abstract: Deep reinforcement learning has shown promising results in learning control policies for complex sequential decision-making tasks. However, these neural network-based policies are known to be vulnerable to adversarial examples. This vulnerability poses a potentially serious threat to safety-critical systems such as autonomous vehicles. In this paper, we propose a defense mechanism to defend reinforcement learning agents from adversarial attacks by leveraging an action-conditioned frame prediction module. Our core idea is that the adversarial examples targeting at a neural network-based policy are not effective for the frame prediction model. By comparing the action distribution produced by a policy from processing the current observed frame to the action distribution produced by the same policy from processing the predicted frame from the action-conditioned frame prediction module, we can detect the presence of adversarial examples. Beyond detecting the presence of adversarial examples, our method allows the agent to continue performing the task using the predicted frame when the agent is under attack. We evaluate the performance of our algorithm using five games in Atari 2600. Our results demonstrate that the proposed defense mechanism achieves favorable performance against baseline algorithms in detecting adversarial examples and in earning rewards when the agents are under attack.

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 "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"
    - "whatever does not kill deep reinforcement learning makes it stronger"

Connected through hyperedge HE-3 (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-4 (induced by source paper "explanation methods in deep learning users values concerns and challenges"):
  with members:
    - "methods for interpreting and understanding deep neural networks"
    - "deep learning a critical appraisal"
    - "audio adversarial examples targeted attacks on speech to text"
    - "beyond word importance contextual decomposition to extract interactions from lstms"
    - "towards interpretable r cnn by unfolding latent structures"
    - "interpretable explorable approximations of black box models"
    - "recurrent relational networks"
    - "a simple neural network module for relational reasoning"

Connected through hyperedge HE-5 (induced by source paper "deflecting adversarial attacks with pixel deflection"):
  with members:
    - "one pixel attack for fooling deep neural networks"
    - "feature squeezing detecting adversarial examples in deep neural networks"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "keeping the bad guys out protecting and vaccinating deep learning with jpeg compression"
    - "magnet a two pronged defense against adversarial examples"
    - "countering adversarial images using input transformations"
    - "defense against universal adversarial perturbations"
    - "dirty pixels optimizing image classification architectures for raw sensor data"

Connected through hyperedge HE-6 (induced by source paper "sequential attacks on agents for long term adversarial goals"):
  with members:
    - "adversarial attacks on neural network policies"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "robust deep reinforcement learning with adversarial attacks"
    - "robust adversarial reinforcement learning"
    - "vulnerability of deep reinforcement learning to policy induction attacks"
    - "learning to fly by crashing"
    - "boosting adversarial attacks with momentum"
    - "adversarial image perturbation for privacy protection a game theory perspective"

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"
    - "whatever does not kill deep reinforcement learning makes it stronger"
    - "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 "whatever does not kill deep reinforcement learning makes it stronger"):
  with members:
    - "adversarial attacks on neural network policies"
    - "parameter space noise for exploration"
    - "vulnerability of deep reinforcement learning to policy induction attacks"
    - "noisy networks for exploration"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "practical black box attacks against machine learning"
    - "learning deep control policies for autonomous aerial vehicles with mpc guided policy search"
    - "intriguing properties of 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.CV(Computer Vision and Pattern Recognition)