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 attacks on neural network policies"
  Abstract: Machine learning classifiers are known to be vulnerable to inputs maliciously constructed by adversaries to force misclassification. Such adversarial examples have been extensively studied in the context of computer vision applications. In this work, we show adversarial attacks are also effective when targeting neural network policies in reinforcement learning. Specifically, we show existing adversarial example crafting techniques can be used to significantly degrade test-time performance of trained policies. Our threat model considers adversaries capable of introducing small perturbations to the raw input of the policy. We characterize the degree of vulnerability across tasks and training algorithms, for a subclass of adversarial-example attacks in white-box and black-box settings. Regardless of the learned task or training algorithm, we observe a significant drop in performance, even with small adversarial perturbations that do not interfere with human perception. Videos are available at this http URL.

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 "when wireless security meets machine learning motivation challenges and research directions"):
  with members:
    - "real time and embedded deep learning on fpga for rf signal classification"
    - "adversarial examples in rf deep learning detection of the attack and its physical robustness"
    - "physical adversarial attacks against end to end autoencoder communication systems"
    - "adversarial deep learning for over the air spectrum poisoning attacks"
    - "trojan attacks on wireless signal classification with adversarial machine learning"
    - "deepwifi cognitive wifi with deep learning"
    - "when attackers meet ai learning empowered attacks in cooperative spectrum sensing"
    - "generative adversarial networks for black box api attacks with limited training data"

Connected through hyperedge HE-4 (induced by source paper "adversarial machine learning perspectives from adversarial risk analysis"):
  with members:
    - "estimating counterfactual treatment outcomes over time through adversarially balanced representations"
    - "gradient methods for solving stackelberg games"
    - "opponent aware reinforcement learning"
    - "knowing when to stop evaluation and verification of conformity to output size specifications"
    - "gltr statistical detection and visualization of generated text"
    - "a survey of game theoretic approaches for adversarial machine learning in cybersecurity tasks"
    - "on evaluating adversarial robustness"
    - "wild patterns ten years after the rise of adversarial machine learning"

Connected through hyperedge HE-5 (induced by source paper "deep adversarial reinforcement learning for object disentangling"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "dota 2 with large scale deep reinforcement learning"
    - "risk averse robust adversarial reinforcement learning"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "automatic goal generation for reinforcement learning agents"
    - "robust deep reinforcement learning with adversarial attacks"
    - "robust adversarial reinforcement learning"
    - "reverse curriculum generation for reinforcement learning"

Connected through hyperedge HE-6 (induced by source paper "copycat taking control of neural policies with constant attacks"):
  with members:
    - "dopamine a research framework for deep reinforcement learning"
    - "an atari model zoo for analyzing visualizing and comparing deep reinforcement learning agents"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "robust deep reinforcement learning with adversarial attacks"
    - "robust adversarial reinforcement learning"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"
    - "end to end learning for self driving cars"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-7 (induced by source paper "action manipulation attacks against stochastic bandits attacks and defense"):
  with members:
    - "robust stochastic bandit algorithms under probabilistic unbounded adversarial attack"
    - "data poisoning attacks on stochastic bandits"
    - "the intrinsic robustness of stochastic bandits to strategic manipulation"
    - "stochastic bandits robust to adversarial corruptions"
    - "data poisoning attacks in contextual bandits"
    - "is feature selection secure against training data poisoning"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "data poisoning attacks on factorization based collaborative filtering"

Connected through hyperedge HE-8 (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"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "flow a modular learning framework for autonomy in traffic"
    - "vulnerability of deep reinforcement learning to policy induction attacks"

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)