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: "large scale kernel learning using block coordinate descent"
  Abstract: We demonstrate that distributed block coordinate descent can quickly solve kernel regression and classification problems with millions of data points. Armed with this capability, we conduct a thorough comparison between the full kernel, the Nystr\"om method, and random features on three large classification tasks from various domains. Our results suggest that the Nystr\"om method generally achieves better statistical accuracy than random features, but can require significantly more iterations of optimization. Lastly, we derive new rates for block coordinate descent which support our experimental findings when specialized to kernel methods.

Connected through hyperedge HE-1 (induced by source paper "beyond least squares fast rates for regularized empirical risk minimization through self concordance"):
  with members:
    - "statistical optimality of stochastic gradient descent on hard learning problems through multiple passes"
    - "logistic regression the importance of being improper"
    - "fast rates with high probability in exp concave statistical learning"
    - "fast rates in statistical and online learning"
    - "on the complexity of learning with kernels"
    - "logistic regression tight bounds for stochastic and online optimization"
    - "self concordant analysis for logistic regression"

Connected through hyperedge HE-2 (induced by source paper "low precision random fourier features for memory constrained kernel approximation"):
  with members:
    - "high accuracy low precision training"
    - "gaussian quadrature for kernel features"
    - "orthogonal random features"
    - "recursive sampling for the nystr om method"
    - "on the error of random fourier features"
    - "deep learning with limited numerical precision"
    - "deep fried convnets"
    - "revisiting the nystrom method for improved large scale machine learning"

Connected through hyperedge HE-3 (induced by source paper "efficient data driven geologic feature characterization from pre stack seismic measurements using randomized machine learning algorithm"):
  with members:
    - "scalable kernel k means clustering with nystrom approximation relative error bounds"
    - "hierarchically compositional kernels for scalable nonparametric learning"
    - "faster kernel ridge regression using sketching and preconditioning"
    - "towards more efficient spsd matrix approximation and cur matrix decomposition"
    - "a practical guide to randomized matrix computations with matlab implementations"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "sharp analysis of low rank kernel matrix approximations"
    - "faster least squares approximation"

Connected through hyperedge HE-4 (induced by source paper "accelerating large scale data analysis by offloading to high performance computing libraries using alchemist"):
  with members:
    - "alchemist an apache spark mpi interface"
    - "building near real time processing pipelines with the spark mpi platform"
    - "matrix factorization at scale a comparison of scientific data analytics in spark and c mpi using three case studies"

Connected through hyperedge HE-5 (induced by source paper "a convex method for classification of groups of examples"):
  with members:
    - "rich feature hierarchies for accurate object detection and semantic segmentation"

Connected through hyperedge HE-6 (induced by source paper "recursive sampling for the nystr om method"):
  with members:
    - "relative error embeddings for the gaussian kernel distance"
    - "fast dpp sampling for nystr om with application to kernel methods"
    - "on column selection in approximate kernel canonical correlation analysis"
    - "ridge leverage scores for low rank approximation"
    - "input sparsity time low rank approximation via ridge leverage score sampling"
    - "streaming kernel principal component analysis"
    - "frequent directions simple and deterministic matrix sketching"
    - "optimal principal component analysis in distributed and streaming models"

Connected through hyperedge HE-7 (induced by source paper "faster kernel ridge regression using sketching and preconditioning"):
  with members:
    - "sharper bounds for regression and low rank approximation with regularization"
    - "hierarchically compositional kernels for scalable nonparametric learning"
    - "optimal approximate matrix product in terms of stable rank"
    - "implementing randomized matrix algorithms in parallel and distributed environments"
    - "sketching as a tool for numerical linear algebra"
    - "scalable kernel methods via doubly stochastic gradients"
    - "preconditioned krylov solvers for kernel regression"
    - "revisiting the nystrom method for improved large scale machine learning"

Connected through hyperedge HE-8 (induced by source paper "communication efficient parallel block minimization for kernel machines"):
  with members:
    - "a distributed algorithm for training nonlinear kernel machines"
    - "fastfood approximate kernel expansions in loglinear time"
    - "a distributed block coordinate descent method for training l_1 regularized linear classifiers"
    - "a divide and conquer solver for kernel support vector machines"

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)