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: "efficient parallel methods for deep reinforcement learning"
  Abstract: We propose a novel framework for efficient parallelization of deep reinforcement learning algorithms, enabling these algorithms to learn from multiple actors on a single machine. The framework is algorithm agnostic and can be applied to on-policy, off-policy, value based and policy gradient based algorithms. Given its inherent parallelism, the framework can be efficiently implemented on a GPU, allowing the usage of powerful models while significantly reducing training time. We demonstrate the effectiveness of our framework by implementing an advantage actor-critic algorithm on a GPU, using on-policy experiences and employing synchronous updates. Our algorithm achieves state-of-the-art performance on the Atari domain after only a few hours of training. Our framework thus opens the door for much faster experimentation on demanding problem domains. Our implementation is open-source and is made public at this https URL

Connected through hyperedge HE-1 (induced by source paper "population guided parallel policy search for reinforcement learning"):
  with members:
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "cem rl combining evolutionary and gradient based methods for policy search"
    - "scalable coordinated exploration in concurrent reinforcement learning"
    - "self imitation learning"
    - "coordinated exploration in concurrent reinforcement learning"

Connected through hyperedge HE-2 (induced by source paper "generalized off policy actor critic"):
  with members:
    - "off policy policy gradient with state distribution correction"
    - "off policy deep reinforcement learning by bootstrapping the covariate shift"
    - "an off policy policy gradient theorem using emphatic weightings"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "convergent actor critic algorithms under off policy training and function approximation"
    - "online off policy prediction"
    - "addressing function approximation error in actor critic methods"

Connected through hyperedge HE-3 (induced by source paper "multi vehicle mixed reality reinforcement learning for autonomous multi lane driving"):
  with members:
    - "a fleet of miniature cars for experiments in cooperative driving"
    - "learning safe unlabeled multi robot planning with motion constraints"
    - "sim to multi real transfer of low level robust control policies to multiple quadrotors"
    - "deepracer educational autonomous racing platform for experimentation with sim2real reinforcement learning"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "sim to real learning agile locomotion for quadruped robots"
    - "learning to adapt in dynamic real world environments through meta reinforcement learning"
    - "closing the sim to real loop adapting simulation randomization with real world experience"

Connected through hyperedge HE-4 (induced by source paper "trojdrl trojan attacks on deep reinforcement learning agents"):
  with members:
    - "detecting backdoor attacks on deep neural networks by activation clustering"
    - "fine pruning defending against backdooring attacks on deep neural networks"
    - "spectral signatures in backdoor attacks"
    - "poison frogs targeted clean label poisoning attacks on neural networks"
    - "adversarial perturbations against real time video classification systems"
    - "obfuscated gradients give a false sense of security circumventing defenses to adversarial examples"
    - "adversarial attacks on neural network policies"
    - "robust physical world attacks on deep learning models"

Connected through hyperedge HE-5 (induced by source paper "gpu accelerated atari emulation for reinforcement learning"):
  with members:
    - "deepmind control suite"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "accelerated methods for deep reinforcement learning"
    - "distributed prioritized experience replay"
    - "rainbow combining improvements in deep reinforcement learning"
    - "elf an extensive lightweight and flexible research platform for real time strategy games"
    - "a distributional perspective on reinforcement learning"
    - "noisy networks for exploration"

Connected through hyperedge HE-6 (induced by source paper "dac the double actor critic architecture for learning options"):
  with members:
    - "generalized off policy actor critic"
    - "deepmind control suite"
    - "learning abstract options"
    - "learning latent dynamics for planning from pixels"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "ace an actor ensemble algorithm for continuous control with tree search"
    - "eigenoption discovery through the deep successor representation"
    - "feudal networks for hierarchical reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "continual and multi task reinforcement learning with shared episodic memory"):
  with members:
    - "sim to real transfer of robotic control with dynamics randomization"
    - "meta learning shared hierarchies"
    - "gated attention architectures for task oriented language grounding"
    - "neural map structured memory for deep reinforcement learning"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "neural episodic control"
    - "mapping instructions and visual observations to actions with reinforcement learning"
    - "stochastic neural networks for hierarchical reinforcement learning"

Connected through hyperedge HE-8 (induced by source paper "gossip based actor learner architectures for deep reinforcement learning"):
  with members:
    - "elf opengo an analysis and open reimplementation of alphazero"
    - "asynchronous subgradient push"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "accelerated methods for deep reinforcement learning"
    - "addressing function approximation error in actor critic methods"
    - "distributed prioritized experience replay"
    - "stochastic gradient push for distributed deep learning"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"

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)