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: "austerity in mcmc land cutting the metropolis hastings budget"
  Abstract: Can we make Bayesian posterior MCMC sampling more efficient when faced with very large datasets? We argue that computing the likelihood for N datapoints in the Metropolis-Hastings (MH) test to reach a single binary decision is computationally inefficient. We introduce an approximate MH rule based on a sequential hypothesis test that allows us to accept or reject samples with high confidence using only a fraction of the data required for the exact MH rule. While this method introduces an asymptotic bias, we show that this bias can be controlled and is more than offset by a decrease in variance due to our ability to draw more samples per unit of time.

Connected through hyperedge HE-1 (induced by source paper "cyclical stochastic gradient mcmc for bayesian deep learning"):
  with members:
    - "averaging weights leads to wider optima and better generalization"
    - "snapshot ensembles train 1 get m for free"
    - "non convex learning via stochastic gradient langevin dynamics a nonasymptotic analysis"
    - "a hitting time analysis of stochastic gradient langevin dynamics"
    - "super convergence very fast training of residual networks using large learning rates"
    - "bayesian recurrent neural networks"
    - "sgdr stochastic gradient descent with restarts"
    - "plug play generative networks conditional iterative generation of images in latent space"

Connected through hyperedge HE-2 (induced by source paper "minibatch gibbs sampling on large graphical models"):
  with members:
    - "holoclean holistic data repairs with probabilistic inference"
    - "dimmwitted a study of main memory statistical analytics"

Connected through hyperedge HE-3 (induced by source paper "an efficient minibatch acceptance test for metropolis hastings"):
  with members:
    - "online but accurate inference for latent variable models with local gibbs sampling"
    - "bayesian posterior sampling via stochastic gradient fisher scoring"

Connected through hyperedge HE-4 (induced by source paper "distributed bayesian learning with stochastic natural gradient expectation propagation and the posterior server"):
  with members:
    - "stuck in a what adventures in weight space"
    - "overcoming catastrophic forgetting in neural networks"
    - "adam a method for stochastic optimization"
    - "neural variational inference and learning in belief networks"
    - "identifying and attacking the saddle point problem in high dimensional non convex optimization"
    - "expectation propogation for approximate inference in dynamic bayesian networks"

Connected through hyperedge HE-5 (induced by source paper "large scale distributed bayesian matrix factorization using stochastic gradient mcmc"):
  with members:
    - "julia a fresh approach to numerical computing"
    - "bayesian posterior sampling via stochastic gradient fisher scoring"

Connected through hyperedge HE-6 (induced by source paper "big learning with bayesian methods"):
  with members:
    - "saberlda sparsity aware learning of topic models on gpus"
    - "max margin nonparametric latent feature models for link prediction"
    - "theano a python framework for fast computation of mathematical expressions"
    - "model parallel inference for big topic models"
    - "neural variational inference and learning in belief networks"
    - "exploiting the statistics of learning and inference"
    - "caffe convolutional architecture for fast feature embedding"
    - "peacock learning long tail topic features for industrial applications"

Connected through hyperedge HE-7 (induced by source paper "exploiting the statistics of learning and inference"):
  with members:
    - "gps abc gaussian process surrogate approximate bayesian computation"
    - "expectation propagation for approximate bayesian inference"
    - "improving neural networks by preventing co adaptation of feature detectors"
    - "bayesian posterior sampling via stochastic gradient fisher scoring"

Connected through hyperedge HE-8 (induced by source paper "mean field networks"):
  with members:
    - "neural variational inference and learning in belief networks"
    - "learning graphical model parameters with approximate marginal inference"

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)