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: "randomized value functions via multiplicative normalizing flows"
  Abstract: Randomized value functions offer a promising approach towards the challenge of efficient exploration in complex environments with high dimensional state and action spaces. Unlike traditional point estimate methods, randomized value functions maintain a posterior distribution over action-space values. This prevents the agent's behavior policy from prematurely exploiting early estimates and falling into local optima. In this work, we leverage recent advances in variational Bayesian neural networks and combine these with traditional Deep Q-Networks (DQN) and Deep Deterministic Policy Gradient (DDPG) to achieve randomized value functions for high-dimensional domains. In particular, we augment DQN and DDPG with multiplicative normalizing flows in order to track a rich approximate posterior distribution over the parameters of the value function. This allows the agent to perform approximate Thompson sampling in a computationally efficient manner via stochastic gradient methods. We demonstrate the benefits of our approach through an empirical comparison in high dimensional environments.

Connected through hyperedge HE-1 (induced by source paper "benchmarking bonus based exploration methods on the arcade learning environment"):
  with members:
    - "dopamine a research framework for deep reinforcement learning"
    - "exploration by random network distillation"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "large scale study of curiosity driven learning"
    - "dora the explorer directed outreaching reinforcement action selection"
    - "rainbow combining improvements in deep reinforcement learning"
    - "deep reinforcement learning that matters"
    - "the uncertainty bellman equation and exploration"

Connected through hyperedge HE-2 (induced by source paper "attraction repulsion actor critic for continuous control reinforcement learning"):
  with members:
    - "collaborative evolutionary reinforcement learning"
    - "leveraging exploration in off policy algorithms via normalizing flows"
    - "cem rl combining evolutionary and gradient based methods for policy search"
    - "evolution guided policy gradient in reinforcement learning"
    - "meta reinforcement learning of structured exploration strategies"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "boosting trust region policy optimization by normalizing flows policy"
    - "bayesian policy gradients via alpha divergence dropout inference"

Connected through hyperedge HE-3 (induced by source paper "stochastic neural network with kronecker flow"):
  with members:
    - "functional variational bayesian neural networks"
    - "pac bayes with backprop"
    - "neural importance sampling"
    - "invertible residual networks"
    - "stronger generalization bounds for deep nets via a compression approach"
    - "efficient exploration through bayesian deep q networks"
    - "exploring generalization in deep learning"
    - "noisy natural gradient as variational inference"

Connected through hyperedge HE-4 (induced by source paper "worst case regret bounds for exploration via randomized value functions"):
  with members:
    - "randomised bayesian least squares policy iteration"
    - "exploration by random network distillation"
    - "is q learning provably efficient"
    - "efficient exploration through bayesian deep q networks"
    - "noisy networks for exploration"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "deep exploration via bootstrapped dqn"
    - "thompson sampling for contextual bandits with linear payoffs"

Connected through hyperedge HE-5 (induced by source paper "parameterized indexed value function for efficient exploration in reinforcement learning"):
  with members:
    - "behaviour suite for reinforcement learning"
    - "the uncertainty bellman equation and exploration"
    - "a tutorial on thompson sampling"
    - "deep exploration via bootstrapped dqn"
    - "deep reinforcement learning with a natural language action space"
    - "bayesian reinforcement learning a survey"
    - "efficient bayes adaptive reinforcement learning using sample based search"
    - "analysis of thompson sampling for the multi armed bandit problem"

Connected through hyperedge HE-6 (induced by source paper "randomised bayesian least squares policy iteration"):
  with members:
    - "exploration by distributional reinforcement learning"
    - "efficient exploration through bayesian deep q networks"
    - "bayesian reinforcement learning a survey"

Connected through hyperedge HE-7 (induced by source paper "off policy deep reinforcement learning without exploration"):
  with members:
    - "reinforcement learning from imperfect demonstrations"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "residual policy learning"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "representation balancing mdps for off policy policy evaluation"
    - "algorithmic framework for model based deep reinforcement learning with theoretical guarantees"
    - "constrained exploration and recovery from experience shaping"

Connected through hyperedge HE-8 (induced by source paper "successor uncertainties exploration and uncertainty in temporal difference learning"):
  with members:
    - "go explore a new approach for hard exploration problems"
    - "count based exploration with the successor representation"
    - "information directed exploration for deep reinforcement learning"
    - "efficient exploration through bayesian deep q networks"
    - "parameter space noise for exploration"
    - "shallow updates for deep reinforcement learning"
    - "eigenoption discovery through the deep successor representation"
    - "the uncertainty bellman equation and exploration"

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)