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: "relative entropy regularized policy iteration"
  Abstract: We present an off-policy actor-critic algorithm for Reinforcement Learning (RL) that combines ideas from gradient-free optimization via stochastic search with learned action-value function. The result is a simple procedure consisting of three steps: i) policy evaluation by estimating a parametric action-value function; ii) policy improvement via the estimation of a local non-parametric policy; and iii) generalization by fitting a parametric policy. Each step can be implemented in different ways, giving rise to several algorithm variants. Our algorithm draws on connections to existing literature on black-box optimization and 'RL as an inference' and it can be seen either as an extension of the Maximum a Posteriori Policy Optimisation algorithm (MPO) [Abdolmaleki et al., 2018a], or as an extension of Trust Region Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES) [Abdolmaleki et al., 2017b; Hansen et al., 1997] to a policy iteration scheme. Our comparison on 31 continuous control tasks from parkour suite [Heess et al., 2017], DeepMind control suite [Tassa et al., 2018] and OpenAI Gym [Brockman et al., 2016] with diverse properties, limited amount of compute and a single set of hyperparameters, demonstrate the effectiveness of our method and the state of art results. Videos, summarizing results, can be found at goo.gl/HtvJKR .

Connected through hyperedge HE-1 (induced by source paper "continuous discrete reinforcement learning for hybrid control in robotics"):
  with members:
    - "regularized hierarchical policies for compositional transfer in robotics"
    - "hybrid actor critic reinforcement learning in parameterized action space"
    - "dac the double actor critic architecture for learning options"
    - "multi pass q networks for deep reinforcement learning with parameterised action spaces"
    - "the termination critic"
    - "deepmind control suite"
    - "learning dexterous in hand manipulation"
    - "soft actor critic algorithms and applications"

Connected through hyperedge HE-2 (induced by source paper "imagined value gradients model based policy optimization with transferable latent dynamics models"):
  with members:
    - "exploiting hierarchy for learning and transfer in kl regularized rl"
    - "cobra data efficient model based rl through unsupervised object discovery and curiosity driven exploration"
    - "model based reinforcement learning for atari"
    - "deepmdp learning continuous latent space models for representation learning"
    - "information asymmetry in kl regularized rl"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "combined reinforcement learning via abstract representations"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-3 (induced by source paper "stochastic latent actor critic deep reinforcement learning with a latent variable model"):
  with members:
    - "generating diverse high fidelity images with vq vae 2"
    - "deepmdp learning continuous latent space models for representation learning"
    - "shaping belief states with generative environment models for rl"
    - "deepmind control suite"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "learning latent dynamics for planning from pixels"
    - "visual reinforcement learning with imagined goals"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"

Connected through hyperedge HE-4 (induced by source paper "regularized hierarchical policies for compositional transfer in robotics"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "exploiting hierarchy for learning and transfer in kl regularized rl"
    - "multitask soft option learning"
    - "dac the double actor critic architecture for learning options"
    - "information asymmetry in kl regularized rl"
    - "the termination critic"
    - "deepmind control suite"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"

Connected through hyperedge HE-5 (induced by source paper "stabilizing transformers for reinforcement learning"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "meta reinforcement learning as task inference"
    - "transformer xl attentive language models beyond a fixed length context"
    - "v mpo on policy maximum a posteriori policy optimization for discrete and continuous control"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "multi task deep reinforcement learning with popart"
    - "generating wikipedia by summarizing long sequences"

Connected through hyperedge HE-6 (induced by source paper "v mpo on policy maximum a posteriori policy optimization for discrete and continuous control"):
  with members:
    - "tf replicator distributed machine learning for researchers"
    - "off policy actor critic with shared experience replay"
    - "transformer xl attentive language models beyond a fixed length context"
    - "human level performance in 3d multiplayer games with population based reinforcement learning"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "supervised policy update for deep reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "multi task deep reinforcement learning with popart"

Connected through hyperedge HE-7 (induced by source paper "robust reinforcement learning for continuous control with model misspecification"):
  with members:
    - "challenges of real world reinforcement learning"
    - "a bayesian approach to robust reinforcement learning"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "deepmind control suite"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "soft actor critic algorithms and applications"
    - "unicorn continual learning with a universal off policy agent"

Connected through hyperedge HE-8 (induced by source paper "discrete and continuous action representation for practical rl in video games"):
  with members:
    - "distilling policy distillation"
    - "soft actor critic for discrete action settings"
    - "discretizing continuous action space for on policy optimization"
    - "improving exploration in soft actor critic with normalizing flows policies"
    - "leveraging exploration in off policy algorithms via normalizing flows"
    - "managing engineering systems with large state and action spaces through deep reinforcement learning"
    - "hierarchical approaches for reinforcement learning in parameterized action space"
    - "soft actor critic algorithms and applications"

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)