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: "the reactor a fast and sample efficient actor critic agent for reinforcement learning"
  Abstract: In this work we present a new agent architecture, called Reactor, which combines multiple algorithmic and architectural contributions to produce an agent with higher sample-efficiency than Prioritized Dueling DQN (Wang et al., 2016) and Categorical DQN (Bellemare et al., 2017), while giving better run-time performance than A3C (Mnih et al., 2016). Our first contribution is a new policy evaluation algorithm called Distributional Retrace, which brings multi-step off-policy updates to the distributional reinforcement learning setting. The same approach can be used to convert several classes of multi-step policy evaluation algorithms designed for expected value evaluation into distributional ones. Next, we introduce the \b{eta}-leave-one-out policy gradient algorithm which improves the trade-off between variance and bias by using action values as a baseline. Our final algorithmic contribution is a new prioritized replay algorithm for sequences, which exploits the temporal locality of neighboring observations for more efficient replay prioritization. Using the Atari 2600 benchmarks, we show that each of these innovations contribute to both the sample efficiency and final agent performance. Finally, we demonstrate that Reactor reaches state-of-the-art performance after 200 million frames and less than a day of training.

Connected through hyperedge HE-1 (induced by source paper "review analyze and design a comprehensive deep reinforcement learning framework"):
  with members:
    - "deep reinforcement learning for cyber security"
    - "pytorch an imperative style high performance deep learning library"
    - "dopamine a research framework for deep reinforcement learning"
    - "deep reinforcement learning for multi agent systems a review of challenges solutions and applications"
    - "large scale study of curiosity driven learning"
    - "accelerated methods for deep reinforcement learning"
    - "relational recurrent neural networks"
    - "distributed prioritized experience replay"

Connected through hyperedge HE-2 (induced by source paper "taylor expansion policy optimization"):
  with members:
    - "dota 2 with large scale deep reinforcement learning"
    - "adaptive trade offs in off policy learning"
    - "v mpo on policy maximum a posteriori policy optimization for discrete and continuous control"
    - "policy optimization through approximated importance sampling"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "observe and look further achieving consistent performance on atari"
    - "distributed prioritized experience replay"
    - "policy optimization via importance sampling"

Connected through hyperedge HE-3 (induced by source paper "resource management in wireless networks via multi agent deep reinforcement learning"):
  with members:
    - "spectrum sharing in vehicular networks based on multi agent reinforcement learning"
    - "dota 2 with large scale deep reinforcement learning"
    - "on the power of curriculum learning in training deep networks"
    - "a deep q learning method for downlink power allocation in multi cell networks"
    - "deep reinforcement learning for distributed uncoordinated cognitive radios resource allocation"
    - "zpd teaching strategies for deep reinforcement learning from demonstrations"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "quantifying generalization in reinforcement learning"

Connected through hyperedge HE-4 (induced by source paper "sample based distributional policy gradient"):
  with members:
    - "gan based deep distributional reinforcement learning for resource management in network slicing"
    - "deepmind control suite"
    - "quota the quantile option architecture for reinforcement learning"
    - "nonlinear distributional gradient temporal difference learning"
    - "implicit quantile networks for distributional reinforcement learning"
    - "distributed distributional deterministic policy gradients"
    - "generative modeling using the sliced wasserstein distance"
    - "distributional multivariate policy evaluation and exploration with the bellman gan"

Connected through hyperedge HE-5 (induced by source paper "go explore a new approach for hard exploration problems"):
  with members:
    - "fractal ai a fragile theory of intelligence"
    - "exploration by random network distillation"
    - "backplay man muss immer umkehren"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "learning dexterous in hand manipulation"
    - "fast exploration with simplified models and approximately optimistic planning in model based reinforcement learning"
    - "deep curiosity search intra life exploration can improve performance on challenging deep reinforcement learning problems"
    - "playing hard exploration games by watching youtube"

Connected through hyperedge HE-6 (induced by source paper "direct and indirect reinforcement learning"):
  with members:
    - "generalized off policy actor critic"
    - "an off policy policy gradient theorem using emphatic weightings"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "model ensemble trust region policy optimization"
    - "self imitation learning"
    - "addressing function approximation error in actor critic methods"
    - "distributed prioritized experience replay"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-7 (induced by source paper "td regularized actor critic methods"):
  with members:
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "addressing function approximation error in actor critic methods"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "rainbow combining improvements in deep reinforcement learning"
    - "deep reinforcement learning that matters"
    - "f divergence constrained policy improvement"
    - "trust pcl an off policy trust region method for continuous control"
    - "boosting the actor with dual critic"

Connected through hyperedge HE-8 (induced by source paper "off policy actor critic with shared experience replay"):
  with members:
    - "model based reinforcement learning for atari"
    - "ray interference a source of plateaus in deep reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "meta gradient reinforcement learning"
    - "unicorn continual learning with a universal off policy agent"
    - "distributed prioritized experience replay"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "rainbow combining improvements in deep reinforcement learning"

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.AI(Artificial Intelligence)