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: "information asymmetry in kl regularized rl"
  Abstract: Many real world tasks exhibit rich structure that is repeated across different parts of the state space or in time. In this work we study the possibility of leveraging such repeated structure to speed up and regularize learning. We start from the KL regularized expected reward objective which introduces an additional component, a default policy. Instead of relying on a fixed default policy, we learn it from data. But crucially, we restrict the amount of information the default policy receives, forcing it to learn reusable behaviors that help the policy learn faster. We formalize this strategy and discuss connections to information bottleneck approaches and to the variational EM algorithm. We present empirical results in both discrete and continuous action domains and demonstrate that, for certain tasks, learning a default policy alongside the policy can significantly speed up and improve learning.

Connected through hyperedge HE-1 (induced by source paper "periodic intra ensemble knowledge distillation for reinforcement learning"):
  with members:
    - "population guided parallel policy search for reinforcement learning"
    - "knowledge distillation by on the fly native ensemble"
    - "off policy deep reinforcement learning without exploration"
    - "evolution guided policy gradient in reinforcement learning"
    - "soft actor critic algorithms and applications"
    - "learning to walk via deep reinforcement learning"
    - "deep mutual learning"
    - "neural network dynamics for model based deep reinforcement learning with model free fine tuning"

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"
    - "qt opt scalable deep reinforcement learning for vision based robotic manipulation"
    - "combined reinforcement learning via abstract representations"
    - "learning latent dynamics for planning from pixels"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"

Connected through hyperedge HE-3 (induced by source paper "exploiting hierarchy for learning and transfer in kl regularized rl"):
  with members:
    - "learning to share and hide intentions using information regularization"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "learning dexterous in hand manipulation"
    - "dexterous manipulation with deep reinforcement learning efficient general and low cost"
    - "latent space policies for hierarchical reinforcement learning"
    - "diversity is all you need learning skills without a reward function"
    - "kickstarting deep reinforcement learning"

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"
    - "the termination critic"
    - "deepmind control suite"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "near optimal representation learning for hierarchical reinforcement learning"

Connected through hyperedge HE-5 (induced by source paper "countering the effects of lead bias in news summarization via multi stage training and auxiliary losses"):
  with members:
    - "fine tune bert for extractive summarization"
    - "content selection in deep learning models of summarization"
    - "learning to extract coherent summary via deep reinforcement learning"
    - "banditsum extractive summarization as a contextual bandit"
    - "ranking sentences for extractive summarization with reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "don t give me the details just the summary topic aware convolutional neural networks for extreme summarization"
    - "tune a research platform for distributed model selection and training"

Connected through hyperedge HE-6 (induced by source paper "marginalized state distribution entropy regularization in policy optimization"):
  with members:
    - "skew fit state covering self supervised reinforcement learning"
    - "provably efficient maximum entropy exploration"
    - "count based exploration with the successor representation"
    - "addressing function approximation error in actor critic methods"
    - "diversity is all you need learning skills without a reward function"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "vfunc a deep generative model for functions"
    - "understanding the impact of entropy on policy optimization"

Connected through hyperedge HE-7 (induced by source paper "entropy regularization with discounted future state distribution in policy gradient methods"):
  with members:
    - "the value function polytope in reinforcement learning"
    - "provably efficient maximum entropy exploration"
    - "an off policy policy gradient theorem using emphatic weightings"
    - "addressing function approximation error in actor critic methods"
    - "diversity is all you need learning skills without a reward function"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "vfunc a deep generative model for functions"
    - "understanding the impact of entropy on policy optimization"

Connected through hyperedge HE-8 (induced by source paper "ds vic unsupervised discovery of decision states for transfer in rl"):
  with members:
    - "exploration by random network distillation"
    - "learning to share and hide intentions using information regularization"
    - "diversity is all you need learning skills without a reward function"
    - "variational option discovery algorithms"
    - "curiosity driven exploration by self supervised prediction"
    - "distral robust multitask reinforcement learning"
    - "a laplacian framework for option discovery in reinforcement learning"
    - "deep variational information bottleneck"

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)