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: "bayesian posterior sampling via stochastic gradient fisher scoring"
  Abstract: In this paper we address the following question: Can we approximately sample from a Bayesian posterior distribution if we are only allowed to touch a small mini-batch of data-items for every sample we generate?. An algorithm based on the Langevin equation with stochastic gradients (SGLD) was previously proposed to solve this, but its mixing rate was slow. By leveraging the Bayesian Central Limit Theorem, we extend the SGLD algorithm so that at high mixing rates it will sample from a normal approximation of the posterior, while for slow mixing rates it will mimic the behavior of SGLD with a pre-conditioner matrix. As a bonus, the proposed algorithm is reminiscent of Fisher scoring (with stochastic gradients) and as such an efficient optimizer during burn-in.

Connected through hyperedge HE-1 (induced by source paper "amagold amortized metropolis adjustment for efficient stochastic gradient mcmc"):
  with members:
    - "cyclical stochastic gradient mcmc for bayesian deep learning"
    - "minibatch gibbs sampling on large graphical models"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "an efficient minibatch acceptance test for metropolis hastings"
    - "scalable bayesian learning of recurrent neural networks for language modeling"

Connected through hyperedge HE-2 (induced by source paper "improving sampling accuracy of stochastic gradient mcmc methods via non uniform subsampling of gradients"):
  with members:
    - "the case for bayesian deep learning"
    - "importance sampling for minibatches"

Connected through hyperedge HE-3 (induced by source paper "a survey of optimization methods from a machine learning perspective"):
  with members:
    - "stochastic conjugate gradient algorithm with variance reduction"
    - "compatible natural gradient policy search"
    - "on the convergence of adam and beyond"
    - "normalized direction preserving adam"
    - "recent advances in recurrent neural networks"
    - "task agnostic meta learning for few shot learning"
    - "scalable trust region method for deep reinforcement learning using kronecker factored approximation"
    - "prototypical networks for few shot learning"

Connected through hyperedge HE-4 (induced by source paper "on the noisy gradient descent that generalizes as sgd"):
  with members:
    - "a tail index analysis of stochastic gradient noise in deep neural networks"
    - "hessian based analysis of sgd for deep nets dynamics and generalization"
    - "quasi potential as an implicit regularizer for the loss function in the stochastic gradient descent"
    - "interplay between optimization and generalization of stochastic gradient descent with covariance noise"
    - "generalization bounds of stochastic gradient descent for wide and deep neural networks"
    - "three factors influencing minima in sgd"
    - "stochastic gradient descent performs variational inference converges to limit cycles for deep networks"
    - "opening the black box of deep neural networks via information"

Connected through hyperedge HE-5 (induced by source paper "bayesian inference for large scale image classification"):
  with members:
    - "cyclical stochastic gradient mcmc for bayesian deep learning"
    - "fixup initialization residual learning without normalization"
    - "a simple baseline for bayesian uncertainty in deep learning"
    - "self normalizing neural networks"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "weight normalization a simple reparameterization to accelerate training of deep neural networks"
    - "bayesian dark knowledge"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "adaptively preconditioned stochastic gradient langevin dynamics"):
  with members:
    - "adaptive gradient methods with dynamic bound of learning rate"
    - "on the convergence of adam and beyond"
    - "scalable natural gradient langevin dynamics in practice"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "in search of the real inductive bias on the role of implicit regularization in deep learning"

Connected through hyperedge HE-7 (induced by source paper "evaluating bayesian deep learning methods for semantic segmentation"):
  with members:
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "on calibration of modern neural networks"
    - "desire distant future prediction in dynamic scenes with interacting agents"
    - "prostate cancer diagnosis using deep learning with 3d multiparametric mri"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "rethinking atrous convolution for semantic image segmentation"
    - "deep network flow for multi object tracking"
    - "the cityscapes dataset for semantic urban scene understanding"

Connected through hyperedge HE-8 (induced by source paper "bayesian model agnostic meta learning"):
  with members:
    - "recasting gradient based meta learning as hierarchical bayes"
    - "prototypical networks for few shot learning"
    - "stein variational policy gradient"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "model agnostic meta learning for fast adaptation of deep networks"
    - "matching networks for one shot learning"
    - "vime variational information maximizing exploration"
    - "bayesian dark knowledge"

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)