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 directed exploration for deep reinforcement learning"
  Abstract: Efficient exploration remains a major challenge for reinforcement learning. One reason is that the variability of the returns often depends on the current state and action, and is therefore heteroscedastic. Classical exploration strategies such as upper confidence bound algorithms and Thompson sampling fail to appropriately account for heteroscedasticity, even in the bandit setting. Motivated by recent findings that address this issue in bandits, we propose to use Information-Directed Sampling (IDS) for exploration in reinforcement learning. As our main contribution, we build on recent advances in distributional reinforcement learning and propose a novel, tractable approximation of IDS for deep Q-learning. The resulting exploration strategy explicitly accounts for both parametric uncertainty and heteroscedastic observation noise. We evaluate our method on Atari games and demonstrate a significant improvement over alternative approaches.

Connected through hyperedge HE-1 (induced by source paper "sample efficient model free reinforcement learning with off policy critics"):
  with members:
    - "exploration by random network distillation"
    - "dual policy iteration"
    - "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"
    - "rudder return decomposition for delayed rewards"
    - "rainbow combining improvements in deep reinforcement learning"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"

Connected through hyperedge HE-2 (induced by source paper "learning efficient and effective exploration policies with counterfactual meta policy"):
  with members:
    - "exploration by random network distillation"
    - "meta gradient reinforcement learning"
    - "meta reinforcement learning of structured exploration strategies"
    - "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"
    - "learning to explore with meta policy gradient"
    - "meta learning for contextual bandit exploration"
    - "counterfactual multi agent policy gradients"

Connected through hyperedge HE-3 (induced by source paper "estimating risk and uncertainty in deep reinforcement learning"):
  with members:
    - "model predictive policy learning with uncertainty regularization for driving in dense traffic"
    - "exploration by random network distillation"
    - "measuring and characterizing generalization in deep reinforcement learning"
    - "mitigating planner overfitting in model based reinforcement learning"
    - "safe reinforcement learning with model uncertainty estimates"
    - "exploration by distributional reinforcement learning"
    - "implicit quantile networks for distributional reinforcement learning"
    - "deep reinforcement learning in a handful of trials using probabilistic dynamics models"

Connected through hyperedge HE-4 (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"
    - "randomized value functions via multiplicative normalizing flows"
    - "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)