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: "action robust reinforcement learning and applications in continuous control"
  Abstract: A policy is said to be robust if it maximizes the reward while considering a bad, or even adversarial, model. In this work we formalize two new criteria of robustness to action uncertainty. Specifically, we consider two scenarios in which the agent attempts to perform an action $a$, and (i) with probability $\alpha$, an alternative adversarial action $\bar a$ is taken, or (ii) an adversary adds a perturbation to the selected action in the case of continuous action space. We show that our criteria are related to common forms of uncertainty in robotics domains, such as the occurrence of abrupt forces, and suggest algorithms in the tabular case. Building on the suggested algorithms, we generalize our approach to deep reinforcement learning (DRL) and provide extensive experiments in the various MuJoCo domains. Our experiments show that not only does our approach produce robust policies, but it also improves the performance in the absence of perturbations. This generalization indicates that action-robustness can be thought of as implicit regularization in RL problems.

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"
    - "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"
    - "adversary a3c for robust reinforcement learning"

Connected through hyperedge HE-3 (induced by source paper "spatiotemporally constrained action space attacks on deep reinforcement learning agents"):
  with members:
    - "semantic adversarial attacks parametric transformations that fool deep classifiers"
    - "deepmimic example guided deep reinforcement learning of physics based character skills"
    - "differentiable mpc for end to end planning and control"
    - "sequential attacks on agents for long term adversarial goals"
    - "online robust policy learning in the presence of unknown adversaries"
    - "gradient band based adversarial training for generalized attack immunity of a3c path finding"
    - "adversarial attacks on neural network policies"
    - "tactics of adversarial attack on deep reinforcement learning agents"

Connected through hyperedge HE-4 (induced by source paper "wasserstein robust reinforcement learning"):
  with members:
    - "investigating generalisation in continuous deep reinforcement learning"
    - "compatible natural gradient policy search"
    - "non stationary markov decision processes a worst case approach using model based reinforcement learning"
    - "deep lagrangian networks using physics as model prior for deep learning"
    - "beyond confidence regions tight bayesian ambiguity sets for robust mdps"
    - "deep lagrangian networks for end to end learning of energy based control for under actuated systems"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "lipschitz continuity in model based reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "robust reinforcement learning via adversarial training with langevin dynamics"):
  with members:
    - "a variational inequality perspective on generative adversarial nets"
    - "addressing function approximation error in actor critic methods"
    - "finding mixed nash equilibria of generative adversarial networks"
    - "robust adversarial reinforcement learning"
    - "stein variational policy gradient"
    - "proximal policy optimization algorithms"
    - "k server via multiscale entropic regularization"
    - "entropy sgd biasing gradient descent into wide valleys"

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)