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: "robust deep reinforcement learning with adversarial attacks"
  Abstract: This paper proposes adversarial attacks for Reinforcement Learning (RL) and then improves the robustness of Deep Reinforcement Learning algorithms (DRL) to parameter uncertainties with the help of these attacks. We show that even a naively engineered attack successfully degrades the performance of DRL algorithm. We further improve the attack using gradient information of an engineered loss function which leads to further degradation in performance. These attacks are then leveraged during training to improve the robustness of RL within robust control framework. We show that this adversarial training of DRL algorithms like Deep Double Q learning and Deep Deterministic Policy Gradients leads to significant increase in robustness to parameter variations for RL benchmarks such as Cart-pole, Mountain Car, Hopper and Half Cheetah environment.

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 "improving generalization of reinforcement learning with minimax distributional soft actor critic"):
  with members:
    - "addressing value estimation errors in reinforcement learning with a state action return distribution function"
    - "investigating generalisation in continuous deep reinforcement learning"
    - "centralized conflict free cooperation for connected and automated vehicles at intersections by proximal policy optimization"
    - "risk averse robust adversarial reinforcement learning"
    - "a comparative analysis of expected and distributional reinforcement learning"
    - "composable deep reinforcement learning for robotic manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "risk sensitive reinforcement learning a constrained optimization viewpoint"

Connected through hyperedge HE-4 (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"
    - "adversarial attacks on neural network policies"
    - "automatic goal generation for reinforcement learning agents"
    - "robust adversarial reinforcement learning"
    - "reverse curriculum generation for reinforcement learning"

Connected through hyperedge HE-5 (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"
    - "adversarial attacks on neural network policies"
    - "tactics of adversarial attack on deep reinforcement learning agents"
    - "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-6 (induced by source paper "the threat of adversarial attacks on machine learning in network security a survey"):
  with members:
    - "improving network intrusion detection classiﬁers by non payload based exploit independent obfuscations an adversarial approach"
    - "detecting cyberattacks in industrial control systems using convolutional neural networks"
    - "wild patterns ten years after the rise of adversarial machine learning"
    - "an adaptive system for detecting malicious queries in web attacks"
    - "adversarial malware binaries evading deep learning for malware detection in executables"
    - "exploring adversarial examples in malware detection"
    - "is feature selection secure against training data poisoning"
    - "adversarially robust generalization requires more data"

Connected through hyperedge HE-7 (induced by source paper "regularization matters in policy optimization"):
  with members:
    - "investigating generalisation in continuous deep reinforcement learning"
    - "towards robust monocular depth estimation mixing datasets for zero shot cross dataset transfer"
    - "control regularization for reduced variance reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "generalization and regularization in dqn"
    - "meta reinforcement learning of structured exploration strategies"
    - "a study on overfitting in deep reinforcement learning"
    - "structured control nets for deep reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "risk averse robust adversarial reinforcement learning"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "reinforcement and imitation learning for diverse visuomotor skills"
    - "virtual to real reinforcement learning for autonomous driving"
    - "gradient free policy architecture search and adaptation"
    - "parameter space noise for exploration"
    - "leave no trace learning to reset for safe and autonomous reinforcement learning"
    - "robust adversarial reinforcement learning"
    - "domain randomization for transferring deep neural networks from simulation to the real world"

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)