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: "vulnerability of deep reinforcement learning to policy induction attacks"
  Abstract: Deep learning classifiers are known to be inherently vulnerable to manipulation by intentionally perturbed inputs, named adversarial examples. In this work, we establish that reinforcement learning techniques based on Deep Q-Networks (DQNs) are also vulnerable to adversarial input perturbations, and verify the transferability of adversarial examples across different DQN models. Furthermore, we present a novel class of attacks based on this vulnerability that enable policy manipulation and induction in the learning process of DQNs. We propose an attack mechanism that exploits the transferability of adversarial examples to implement policy induction attacks on DQNs, and demonstrate its efficacy and impact through experimental study of a game-learning scenario.

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 "watch your back backdoor attacks in deep reinforcement learning based autonomous vehicle control systems"):
  with members:
    - "dissipation of stop and go waves via control of autonomous vehicles field experiments"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "backdoor embedding in convolutional neural network models via invisible perturbation"
    - "spectral signatures in backdoor attacks"
    - "sequential attacks on agents for long term adversarial goals"
    - "adversarial attacks on neural network policies"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "flow a modular learning framework for autonomy in traffic"

Connected through hyperedge HE-3 (induced by source paper "on the robustness of cooperative multi agent reinforcement learning"):
  with members:
    - "adversarial policies attacking deep reinforcement learning"
    - "blackbox attacks on reinforcement learning agents using approximated temporal information"
    - "the starcraft multi agent challenge"
    - "efficient communication in multi agent reinforcement learning via variance based control"
    - "qmix monotonic value function factorisation for deep multi agent reinforcement learning"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial attacks on neural network policies"
    - "tactics of adversarial attack on deep reinforcement learning agents"

Connected through hyperedge HE-4 (induced by source paper "deep reinforcement learning for cyber security"):
  with members:
    - "deep reinforcement learning for detecting malicious websites"
    - "manipulating soft tissues by deep reinforcement learning for autonomous robotic surgery"
    - "multi agent deep reinforcement learning with human strategies"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "a new tensioning method using deep reinforcement learning for surgical pattern cutting"
    - "relational deep reinforcement learning"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "robust deep reinforcement learning for security and safety in autonomous vehicle systems"

Connected through hyperedge HE-5 (induced by source paper "fda feature disruptive attack"):
  with members:
    - "towards explanation of dnn based prediction with guided feature inversion"
    - "techniques for interpretable machine learning"
    - "adversarial logit pairing"
    - "gray box adversarial training"
    - "deflecting adversarial attacks with pixel deflection"
    - "generalizable data free objective for crafting universal adversarial perturbations"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "progressive neural architecture search"

Connected through hyperedge HE-6 (induced by source paper "a new defense against adversarial images turning a weakness into a strength"):
  with members:
    - "simple black box adversarial attacks"
    - "the odds are odd a statistical test for detecting adversarial examples"
    - "stochastic activation pruning for robust adversarial defense"
    - "characterizing adversarial subspaces using local intrinsic dimensionality"
    - "adversarial logit pairing"
    - "defense gan protecting classifiers against adversarial attacks using generative models"
    - "adversarial vulnerability for any classifier"
    - "are adversarial examples inevitable"

Connected through hyperedge HE-7 (induced by source paper "blackbox attacks on reinforcement learning agents using approximated temporal information"):
  with members:
    - "sitatapatra blocking the transfer of adversarial samples"
    - "stateful detection of black box adversarial attacks"
    - "an algorithmic perspective on imitation learning"
    - "black box adversarial attacks with limited queries and information"
    - "behavioral cloning from observation"
    - "the taboo trap behavioural detection of adversarial samples"
    - "rainbow combining improvements in deep reinforcement learning"
    - "adversarial attacks on neural network policies"

Connected through hyperedge HE-8 (induced by source paper "preventing imitation learning with adversarial policy ensembles"):
  with members:
    - "exploring the limitations of behavior cloning for autonomous driving"
    - "adversarial policies attacking deep reinforcement learning"
    - "dynamics aware unsupervised discovery of skills"
    - "policy poisoning in batch reinforcement learning and control"
    - "learning novel policies for tasks"
    - "causal confusion in imitation learning"
    - "risk aware active inverse reinforcement learning"
    - "interpretable latent spaces for learning from demonstration"

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.LG(Machine Learning)