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: "an efficient minibatch acceptance test for metropolis hastings"
  Abstract: We present a novel Metropolis-Hastings method for large datasets that uses small expected-size minibatches of data. Previous work on reducing the cost of Metropolis-Hastings tests yield variable data consumed per sample, with only constant factor reductions versus using the full dataset for each sample. Here we present a method that can be tuned to provide arbitrarily small batch sizes, by adjusting either proposal step size or temperature. Our test uses the noise-tolerant Barker acceptance test with a novel additive correction variable. The resulting test has similar cost to a normal SGD update. Our experiments demonstrate several order-of-magnitude speedups over previous work.

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"
    - "scalable bayesian learning of recurrent neural networks for language modeling"
    - "bayesian posterior sampling via stochastic gradient fisher scoring"

Connected through hyperedge HE-2 (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-3 (induced by source paper "inferring the optimal policy using markov chain monte carlo"):
  with members:
    - "high dimensional continuous control using generalized advantage estimation"
    - "continuous control with deep reinforcement learning"
    - "trust region policy optimization"
    - "end to end training of deep visuomotor policies"

Connected through hyperedge HE-4 (induced by source paper "scene centric joint parsing of cross view videos"):
  with members:
    - "generating visual explanations"
    - "why should i trust you explaining the predictions of any classifier"
    - "attribute and or grammar for joint parsing of human attributes part and pose"
    - "visual7w grounded question answering in images"
    - "a restricted visual turing test for deep scene and event understanding"
    - "vqa visual question answering"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "cross view action modeling learning and recognition"

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)