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: "policy optimization as wasserstein gradient flows"
  Abstract: Policy optimization is a core component of reinforcement learning (RL), and most existing RL methods directly optimize parameters of a policy based on maximizing the expected total reward, or its surrogate. Though often achieving encouraging empirical success, its underlying mathematical principle on {\em policy-distribution} optimization is unclear. We place policy optimization into the space of probability measures, and interpret it as Wasserstein gradient flows. On the probability-measure space, under specified circumstances, policy optimization becomes a convex problem in terms of distribution optimization. To make optimization feasible, we develop efficient algorithms by numerically solving the corresponding discrete gradient flows. Our technique is applicable to several RL settings, and is related to many state-of-the-art policy-optimization algorithms. Empirical results verify the effectiveness of our framework, often obtaining better performance compared to related algorithms.

Connected through hyperedge HE-1 (induced by source paper "regularizing activations in neural networks via distribution matching with the wasserstein metric"):
  with members:
    - "fixup initialization residual learning without normalization"
    - "improving sequence to sequence learning via optimal transport"
    - "iterative normalization beyond standardization towards efficient whitening"
    - "dropblock a regularization method for convolutional networks"
    - "understanding batch normalization"
    - "decorrelated batch normalization"
    - "measuring and regularizing networks in function space"
    - "mode normalization"

Connected through hyperedge HE-2 (induced by source paper "improving sequence to sequence learning via optimal transport"):
  with members:
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "sequence generation with guider network"
    - "a reinforced topic aware convolutional sequence to sequence model for abstractive text summarization"
    - "distilled wasserstein learning for word embedding and topic modeling"
    - "improving gans using optimal transport"
    - "no metrics are perfect adversarial reward learning for visual storytelling"
    - "generating informative and diverse conversational responses via adversarial information maximization"
    - "improved image captioning via policy gradient optimization of spider"

Connected through hyperedge HE-3 (induced by source paper "probability functional descent a unifying perspective on gans variational inference and reinforcement learning"):
  with members:
    - "a convex duality framework for gans"
    - "adversarial distillation of bayesian neural network posteriors"
    - "on wasserstein reinforcement learning and the fokker planck equation"
    - "sbeed convergent reinforcement learning with nonlinear function approximation"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "boosting the actor with dual critic"
    - "learning from conditional distributions via dual embeddings"
    - "generative adversarial imitation learning"

Connected through hyperedge HE-4 (induced by source paper "learning to score behaviors for guided policy optimization"):
  with members:
    - "distributional reinforcement learning with linear function approximation"
    - "provably robust blackbox optimization for reinforcement learning"
    - "evolvability es scalable and direct optimization of evolvability"
    - "structured evolution with compact architectures for scalable policy optimization"
    - "deepmind control suite"
    - "nonlinear distributional gradient temporal difference learning"
    - "implicit quantile networks for distributional reinforcement learning"
    - "recurrent world models facilitate policy evolution"

Connected through hyperedge HE-5 (induced by source paper "stochastically dominant distributional reinforcement learning"):
  with members:
    - "being optimistic to be conservative quickly learning a cvar policy"
    - "virel a variational inference framework for reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "implicit quantile networks for distributional reinforcement learning"
    - "rainbow combining improvements in deep reinforcement learning"
    - "a distributional perspective on reinforcement learning"
    - "distributional reinforcement learning with quantile regression"
    - "reinforcement learning with deep energy based policies"

Connected through hyperedge HE-6 (induced by source paper "quantile regression deep reinforcement learning"):
  with members:
    - "virel a variational inference framework for reinforcement learning"
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "autoregressive quantile networks for generative modeling"
    - "implicit quantile networks for distributional reinforcement learning"
    - "deep reinforcement learning that matters"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "distributional reinforcement learning with quantile regression"

Connected through hyperedge HE-7 (induced by source paper "learning policies through quantile regression"):
  with members:
    - "impala scalable distributed deep rl with importance weighted actor learner architectures"
    - "soft actor critic algorithms and applications"
    - "latent space policies for hierarchical reinforcement learning"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"
    - "implicit quantile networks for distributional reinforcement learning"
    - "equivalence between policy gradients and soft q learning"
    - "reproducibility of benchmarked deep reinforcement learning tasks for continuous control"
    - "distributional reinforcement learning with quantile regression"

Connected through hyperedge HE-8 (induced by source paper "straight through estimator as projected wasserstein gradient flow"):
  with members:
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "muprop unbiased backpropagation for stochastic neural networks"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"
    - "a kernel method for the two sample problem"

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)