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: "provable bayesian inference via particle mirror descent"
  Abstract: Bayesian methods are appealing in their flexibility in modeling complex data and ability in capturing uncertainty in parameters. However, when Bayes' rule does not result in tractable closed-form, most approximate inference algorithms lack either scalability or rigorous guarantees. To tackle this challenge, we propose a simple yet provable algorithm, \emph{Particle Mirror Descent} (PMD), to iteratively approximate the posterior density. PMD is inspired by stochastic functional mirror descent where one descends in the density space using a small batch of data points at each iteration, and by particle filtering where one uses samples to approximate a function. We prove result of the first kind that, with $m$ particles, PMD provides a posterior density estimator that converges in terms of $KL$-divergence to the true posterior in rate $O(1/\sqrt{m})$. We demonstrate competitive empirical performances of PMD compared to several approximate inference algorithms in mixture models, logistic regression, sparse Gaussian processes and latent Dirichlet allocation on large scale datasets.

Connected through hyperedge HE-1 (induced by source paper "implicit generative modeling for efficient exploration"):
  with members:
    - "self supervised exploration via disagreement"
    - "hypergan a generative model for diverse performant neural networks"
    - "exploration by random network distillation"
    - "learning latent dynamics for planning from pixels"
    - "large scale study of curiosity driven learning"
    - "model based active exploration"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "probability functional descent a unifying perspective on gans variational inference and reinforcement learning"):
  with members:
    - "policy optimization as wasserstein gradient flows"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "exponential family estimation via adversarial dynamics embedding"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "kernel exponential family estimation via doubly dual embedding"
    - "hamiltonian variational auto encoder"
    - "spectral normalization for generative adversarial networks"
    - "monge amp ere flow for generative modeling"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "calibrating energy based generative adversarial networks"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-4 (induced by source paper "meta particle flow for sequential bayesian inference"):
  with members:
    - "monge amp ere flow for generative modeling"
    - "deep sets"
    - "stable architectures for deep neural networks"
    - "neural ordinary differential equations"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "filtering variational objectives"
    - "a geometric view of optimal transportation and generative model"
    - "learning combinatorial optimization algorithms over graphs"

Connected through hyperedge HE-5 (induced by source paper "particle flow bayes rule"):
  with members:
    - "stable architectures for deep neural networks"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "filtering variational objectives"
    - "learning combinatorial optimization algorithms over graphs"
    - "learning to learn by gradient descent by gradient descent"
    - "nonparametric variational inference"

Connected through hyperedge HE-6 (induced by source paper "learning deep hidden nonlinear dynamics from aggregate data"):
  with members:
    - "spectrally normalized margin bounds for neural networks"
    - "improved training of wasserstein gans"
    - "c rnn gan continuous recurrent neural networks with adversarial training"
    - "adam a method for stochastic optimization"
    - "learning nonlinear dynamic models"

Connected through hyperedge HE-7 (induced by source paper "mirror descent search and its acceleration"):
  with members:
    - "proximal policy optimization algorithms"
    - "guided policy search as approximate mirror descent"
    - "trust region policy optimization"
    - "path integral policy improvement with covariance matrix adaptation"
    - "sparse q learning with mirror descent"

Connected through hyperedge HE-8 (induced by source paper "stochastic generative hashing"):
  with members:
    - "polysemous codes"
    - "hashing with binary autoencoders"
    - "quantization based fast inner product search"
    - "muprop unbiased backpropagation for stochastic neural networks"
    - "adam a method for stochastic optimization"
    - "neural variational inference and learning in belief networks"
    - "hashing for similarity search a survey"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"

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)