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: "fast stochastic alternating direction method of multipliers"
  Abstract: In this paper, we propose a new stochastic alternating direction method of multipliers (ADMM) algorithm, which incrementally approximates the full gradient in the linearized ADMM formulation. Besides having a low per-iteration complexity as existing stochastic ADMM algorithms, the proposed algorithm improves the convergence rate on convex problems from $O(\frac 1 {\sqrt{T}})$ to $O(\frac 1 T)$, where $T$ is the number of iterations. This matches the convergence rate of the batch ADMM algorithm, but without the need to visit all the samples in each iteration. Experiments on the graph-guided fused lasso demonstrate that the new algorithm is significantly faster than state-of-the-art stochastic and batch ADMM algorithms.

Connected through hyperedge HE-1 (induced by source paper "adaptive weight decay for deep neural networks"):
  with members:
    - "a simple stochastic variance reduced algorithm with fast convergence rates"
    - "on the insufficiency of existing momentum schemes for stochastic optimization"
    - "zeroth order stochastic variance reduction for nonconvex optimization"
    - "regularizing deep neural networks by noise its interpretation and optimization"
    - "don t decay the learning rate increase the batch size"
    - "the shattered gradients problem if resnets are the answer then what is the question"
    - "sharp minima can generalize for deep nets"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "image super resolution via rl csc when residual learning meets convolutional sparse coding"):
  with members:
    - "deep learning for single image super resolution a brief review"
    - "convolutional dictionary learning a comparative review and new algorithms"
    - "enhanced deep residual networks for single image super resolution"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "memnet a persistent memory network for image restoration"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "image restoration using very deep convolutional encoder decoder networks with symmetric skip connections"
    - "a survey of sparse representation algorithms and applications"

Connected through hyperedge HE-3 (induced by source paper "convolutional dictionary learning acceleration and convergence"):
  with members:
    - "efficient sum of outer products dictionary learning soup dil and its application to inverse problems"
    - "convolutional sparse coding for high dynamic range imaging"
    - "working locally thinking globally part ii stability and algorithms for convolutional sparse coding"
    - "optimization methods for convolutional sparse coding"
    - "a fast patch dictionary method for whole image recovery"
    - "sparse modeling for image and vision processing"

Connected through hyperedge HE-4 (induced by source paper "stochastic primal dual proximal extragradient descent for compositely regularized optimization"):
  with members:
    - "stochastic variance reduced admm"
    - "online alternating direction method"
    - "sparse inverse covariance selection via alternating linearization methods"

Connected through hyperedge HE-5 (induced by source paper "on the iteration complexity analysis of stochastic primal dual hybrid gradient approach with high probability"):
  with members:
    - "online alternating direction method"
    - "sparse inverse covariance selection via alternating linearization methods"

Connected through hyperedge HE-6 (induced by source paper "larger is better the effect of learning rates enjoyed by stochastic optimization with progressive variance reduction"):
  with members:
    - "guaranteed sufficient decrease for variance reduced stochastic gradient descent"
    - "accelerated variance reduced stochastic admm"
    - "fast stochastic variance reduced gradient method with momentum acceleration for machine learning"
    - "nonconvex sparse learning via stochastic optimization with progressive variance reduction"
    - "variance reduced and projection free stochastic optimization"
    - "faster eigenvector computation via shift and invert preconditioning"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "improved svrg for non strongly convex or sum of non convex objectives"

Connected through hyperedge HE-7 (induced by source paper "accelerated variance reduced stochastic admm"):
  with members:
    - "fast and light stochastic admm"
    - "mini batch semi stochastic gradient descent in the proximal setting"
    - "scalable stochastic alternating direction method of multipliers"
    - "online alternating direction method"

Connected through hyperedge HE-8 (induced by source paper "fast convolutional sparse coding in the dual domain"):
  with members:
    - "optimization methods for convolutional sparse coding"
    - "dictionary learning for deblurring and digital zoom"

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)