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: "stein variational policy gradient"
  Abstract: Policy gradient methods have been successfully applied to many complex reinforcement learning problems. However, policy gradient methods suffer from high variance, slow convergence, and inefficient exploration. In this work, we introduce a maximum entropy policy optimization framework which explicitly encourages parameter exploration, and show that this framework can be reduced to a Bayesian inference problem. We then propose a novel Stein variational policy gradient method (SVPG) which combines existing policy gradient methods and a repulsive functional to generate a set of diverse but well-behaved policies. SVPG is robust to initialization and can easily be implemented in a parallel manner. On continuous control problems, we find that implementing SVPG on top of REINFORCE and advantage actor-critic algorithms improves both average return and data efficiency.

Connected through hyperedge HE-1 (induced by source paper "curriculum in gradient based meta reinforcement learning"):
  with members:
    - "solving rubik s cube with a robot hand"
    - "active domain randomization"
    - "skew fit state covering self supervised reinforcement learning"
    - "efficient off policy meta reinforcement learning via probabilistic context variables"
    - "meta world a benchmark and evaluation for multi task and meta reinforcement learning"
    - "mame model agnostic meta exploration"
    - "learning domain randomization distributions for transfer of locomotion policies"
    - "paired open ended trailblazer poet endlessly generating increasingly complex and diverse learning environments and their solutions"

Connected through hyperedge HE-2 (induced by source paper "scalable approximate inference and some applications"):
  with members:
    - "stein variational inference for discrete distributions"
    - "understanding straight through estimator in training activation quantized neural nets"
    - "binary ensemble neural network more bits per network or more networks per bit"
    - "deep probabilistic video compression"
    - "a bandit approach to maximum inner product search"
    - "bnn improved binary network training"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "proximal policy optimization algorithms"

Connected through hyperedge HE-3 (induced by source paper "multi path policy optimization"):
  with members:
    - "collaborative evolutionary reinforcement learning"
    - "diversity inducing policy gradient using maximum mean discrepancy to find a set of diverse policies"
    - "cem rl combining evolutionary and gradient based methods for policy search"
    - "gep pg decoupling exploration and exploitation in deep reinforcement learning algorithms"
    - "evolution guided policy gradient in reinforcement learning"
    - "sample efficient reinforcement learning with stochastic ensemble value expansion"
    - "path consistency learning in tsallis entropy regularized mdps"
    - "addressing function approximation error in actor critic methods"

Connected through hyperedge HE-4 (induced by source paper "implicit generation and generalization in energy based models"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "maximum entropy generators for energy based models"
    - "the gan landscape losses architectures regularization and normalization"
    - "learning dexterous in hand manipulation"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "learning intrinsic image decomposition from watching the world"
    - "spectral normalization for generative adversarial networks"
    - "deep anomaly detection with outlier exposure"

Connected through hyperedge HE-5 (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"
    - "the mirage of action dependent baselines in reinforcement learning"
    - "variance reduction for policy gradient with action dependent factorized baselines"

Connected through hyperedge HE-6 (induced by source paper "active domain randomization"):
  with members:
    - "composable deep reinforcement learning for robotic manipulation"
    - "learning dexterous in hand manipulation"
    - "sim to real transfer of robotic control with dynamics randomization"
    - "generalization and regularization in dqn"
    - "closing the sim to real loop adapting simulation randomization with real world experience"
    - "vadra visual adversarial domain randomization and augmentation"
    - "quantifying generalization in reinforcement learning"
    - "addressing function approximation error in actor critic methods"

Connected through hyperedge HE-7 (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"
    - "randomized value functions via multiplicative normalizing flows"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "learning representations in reinforcement learning an information bottleneck approach"):
  with members:
    - "a geometric perspective on optimal representations for reinforcement learning"
    - "learning latent dynamics for planning from pixels"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "near optimal representation learning for hierarchical reinforcement learning"
    - "reward constrained policy optimization"
    - "state representation learning for control an overview"
    - "addressing function approximation error in actor critic methods"
    - "mine mutual information neural estimation"

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)