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 mirage of action dependent baselines in reinforcement learning"
  Abstract: Policy gradient methods are a widely used class of model-free reinforcement learning algorithms where a state-dependent baseline is used to reduce gradient estimator variance. Several recent papers extend the baseline to depend on both the state and action and suggest that this significantly reduces variance and improves sample efficiency without introducing bias into the gradient estimates. To better understand this development, we decompose the variance of the policy gradient estimator and numerically show that learned state-action-dependent baselines do not in fact reduce variance over a state-dependent baseline in commonly tested benchmark domains. We confirm this unexpected result by reviewing the open-source code accompanying these prior papers, and show that subtle implementation decisions cause deviations from the methods presented in the papers and explain the source of the previously observed empirical gains. Furthermore, the variance decomposition highlights areas for improvement, which we demonstrate by illustrating a simple change to the typical value function parameterization that can significantly improve performance.

Connected through hyperedge HE-1 (induced by source paper "merging deterministic policy gradient estimations with varied bias variance tradeoff for effective deep reinforcement learning"):
  with members:
    - "regularized anderson acceleration for off policy deep reinforcement learning"
    - "policy consolidation for continual reinforcement learning"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "a theory of regularized markov decision processes"
    - "importance resampling for off policy prediction"
    - "lookahead optimizer k steps forward 1 step back"
    - "understanding short horizon bias in stochastic meta optimization"
    - "smoothed action value functions for learning gaussian policies"

Connected through hyperedge HE-2 (induced by source paper "credit assignment techniques in stochastic computation graphs"):
  with members:
    - "total stochastic gradient algorithms and applications in reinforcement learning"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "implicit reparameterization gradients"
    - "learning and querying fast generative models for reinforcement learning"
    - "deep variational reinforcement learning for pomdps"

Connected through hyperedge HE-3 (induced by source paper "p3o policy on policy off policy optimization"):
  with members:
    - "self imitation learning"
    - "addressing function approximation error in actor critic methods"
    - "are deep policy gradient algorithms truly policy gradient algorithms"
    - "deep reinforcement learning that matters"
    - "a distributional perspective on reinforcement learning"
    - "interpolated policy gradient merging on policy and off policy gradient estimation for deep reinforcement learning"
    - "bridging the gap between value and policy based reinforcement learning"
    - "revisiting the arcade learning environment evaluation protocols and open problems for general agents"

Connected through hyperedge HE-4 (induced by source paper "sample efficient policy gradient methods with recursive variance reduction"):
  with members:
    - "neural proximal trust region policy optimization attains globally optimal policy"
    - "an improved convergence analysis of stochastic variance reduced policy gradient"
    - "policy optimization with stochastic mirror descent"
    - "neural temporal difference learning converges to global optima"
    - "neural policy gradient methods global optimality and rates of convergence"
    - "a generalization theory of gradient descent for learning over parameterized deep relu networks"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "gradient descent finds global minima of deep neural networks"

Connected through hyperedge HE-5 (induced by source paper "a unified view of likelihood ratio and reparameterization gradients and an optimal importance sampling scheme"):
  with members:
    - "pipps flexible model based policy search robust to the curse of chaos"
    - "total stochastic gradient algorithms and applications in reinforcement learning"
    - "credit assignment techniques in stochastic computation graphs"
    - "leveraging exploration in off policy algorithms via normalizing flows"
    - "new tricks for estimating gradients of expectations"
    - "clipped action policy gradient"
    - "understanding and correcting pathologies in the training of learned optimizers"
    - "implicit reparameterization gradients"

Connected through hyperedge HE-6 (induced by source paper "sub policy adaptation for hierarchical reinforcement learning"):
  with members:
    - "mcp learning composable hierarchical control with multiplicative compositional policies"
    - "self supervised learning of image embedding for continuous control"
    - "credit assignment techniques in stochastic computation graphs"
    - "hierarchical reinforcement learning with hindsight"
    - "hierarchical visuomotor control of humanoids"
    - "learning dexterous in hand manipulation"
    - "visual reinforcement learning with imagined goals"
    - "latent space policies for hierarchical reinforcement learning"

Connected through hyperedge HE-7 (induced by source paper "trajectory wise control variates for variance reduction in policy gradient methods"):
  with members:
    - "policy optimization with stochastic mirror descent"
    - "contrasting exploration in parameter and action space a zeroth order optimization perspective"
    - "truncated horizon policy search combining reinforcement learning imitation learning"
    - "reward estimation variance elimination in sequential decision processes"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "beyond the one step greedy approach in reinforcement learning"
    - "predictor corrector policy optimization"
    - "fast policy learning through imitation and reinforcement"

Connected through hyperedge HE-8 (induced by source paper "low variance and zero variance baselines for extensive form games"):
  with members:
    - "actor critic policy optimization in partially observable multiagent environments"
    - "variance reduction for policy gradient with action dependent factorized baselines"
    - "depth limited solving for imperfect information games"
    - "variance reduction in monte carlo counterfactual regret minimization vr mccfr for extensive form games using baselines"
    - "safe and nested subgame solving for imperfect information games"
    - "deepstack expert level artificial intelligence in heads up no limit poker"
    - "data efficient off policy policy evaluation for reinforcement learning"
    - "aivat a new variance reduction technique for agent evaluation in imperfect information games"

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)