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: "solving ridge regression using sketched preconditioned svrg"
  Abstract: We develop a novel preconditioning method for ridge regression, based on recent linear sketching methods. By equipping Stochastic Variance Reduced Gradient (SVRG) with this preconditioning process, we obtain a significant speed-up relative to fast stochastic methods such as SVRG, SDCA and SAG.

Connected through hyperedge HE-1 (induced by source paper "optimal sketching bounds for exp concave stochastic minimization"):
  with members:
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "eigenvalue decay implies polynomial time learnability for neural networks"
    - "efficient second order online learning by sketching"
    - "fast rates with high probability in exp concave statistical learning"
    - "from exp concavity to variance control high probability o 1 n rates and high probability online to batch conversion"
    - "average stability is invariant to data preconditioning implications to exp concave empirical risk minimization"
    - "faster kernel ridge regression using sketching and preconditioning"
    - "recursive sampling for the nystr om method"

Connected through hyperedge HE-2 (induced by source paper "large scale constrained linear regression revisited faster algorithms via preconditioning"):
  with members:
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "a practical guide to randomized matrix computations with matlab implementations"
    - "faster sgd using sketched conditioning"
    - "implementing randomized matrix algorithms in parallel and distributed environments"
    - "randomized sketches of convex programs with sharp guarantees"
    - "mini batch primal and dual methods for svms"
    - "recovering the optimal solution by dual random projection"
    - "near optimal column based matrix reconstruction"

Connected through hyperedge HE-3 (induced by source paper "efficient linear bandits through matrix sketching"):
  with members:
    - "efficient second order online learning by sketching"
    - "frequent directions simple and deterministic matrix sketching"
    - "sketching as a tool for numerical linear algebra"
    - "a gang of bandits"
    - "thompson sampling for contextual bandits with linear payoffs"

Connected through hyperedge HE-4 (induced by source paper "spectrum approximation beyond fast matrix multiplication algorithms and hardness"):
  with members:
    - "fast estimation of approximate matrix ranks using spectral densities"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "embeddings of schatten norms with applications to data streams"
    - "improved stochastic trace estimation using mutually unbiased bases"
    - "weighted schatten p norm minimization for image denoising and background subtraction"
    - "sketches for matrix norms faster smaller and more general"
    - "faster eigenvector computation via shift and invert preconditioning"
    - "estimating the trace of the matrix inverse by interpolating from the diagonal of an approximate inverse"

Connected through hyperedge HE-5 (induced by source paper "efficient second order online learning by sketching"):
  with members:
    - "efficient frequent directions algorithm for sparse matrices"
    - "the fast convergence of incremental pca"
    - "scale free algorithms for online linear optimization"
    - "frequent directions simple and deterministic matrix sketching"
    - "faster sgd using sketched conditioning"
    - "sketching as a tool for numerical linear algebra"
    - "a linearly convergent conditional gradient algorithm with applications to online and stochastic optimization"
    - "one pass auc optimization"

Connected through hyperedge HE-6 (induced by source paper "average stability is invariant to data preconditioning implications to exp concave empirical risk minimization"):
  with members:
    - "train faster generalize better stability of stochastic gradient descent"
    - "logistic regression tight bounds for stochastic and online optimization"
    - "the sample complexity of learning linear predictors with the squared loss"
    - "relative loss bounds for on line density estimation with the exponential family of distributions"

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)