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: "improved bounds for the nystrom method with application to kernel classification"
  Abstract: We develop two approaches for analyzing the approximation error bound for the Nystrom method that approximates a positive semidefinite (PSD) matrix by sampling a small set of columns, one based on a concentration inequality for integral operators, and one based on random matrix theory. We show that the approximation error, measured in the spectral norm, can be improved from O(N/√m) to O(N/m1-ρ) in the case of large eigengap, where N is the total number of data points, m is the number of sampled data points, and ρ ∈ (0, 1/2) is a positive constant that characterizes the eigengap. When the eigenvalues of the kernel matrix follow a p-power law, our analysis based on random matrix theory further improves the bound to O(N/mp-1) under an incoherence assumption. We present a kernel classification approach based on the Nystrom method and derive its generalization performance using the improved bound. We show that when the eigenvalues of the kernel matrix follow a p-power law, we can reduce the number of support vectors to N2p/(p2 - 1), which is sublinear in N when p > 1+√2, without seriously sacrificing its generalization performance.

Connected through hyperedge HE-1 (induced by source paper "large scale kernel methods and applications to lifelong robot learning"):
  with members:
    - "generalization properties and implicit regularization for multiple passes sgm"
    - "on the equivalence between quadrature rules and random features"
    - "train faster generalize better stability of stochastic gradient descent"
    - "multimodal deep learning for robust rgb d object recognition"
    - "incremental gradient subgradient and proximal methods for convex optimization a survey"
    - "very deep convolutional networks for large scale image recognition"
    - "return of the devil in the details delving deep into convolutional nets"
    - "efficient algorithms and error analysis for the modified nystrom method"

Connected through hyperedge HE-2 (induced by source paper "matrix infinitely divisible series tail inequalities and applications in optimization"):
  with members:
    - "recycling randomness with structure for sublinear time kernel expansions"
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "an exploration of parameter redundancy in deep networks with circulant projections"
    - "from random matrix theory to coding theory volume of a metric ball in unitary group"
    - "robust estimates of covariance matrices in the large dimensional regime"
    - "on the theorem of uniform recovery of random sampling matrices"

Connected through hyperedge HE-3 (induced by source paper "scalable kernel k means clustering with nystrom approximation relative error bounds"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "fixed rank approximation of a positive semidefinite matrix from streaming data"
    - "input sparsity time low rank approximation via ridge leverage score sampling"
    - "towards more efficient spsd matrix approximation and cur matrix decomposition"
    - "mllib machine learning in apache spark"
    - "the singular value decomposition applications and beyond"
    - "the hardness of approximation of euclidean k means"
    - "sketching as a tool for numerical linear algebra"

Connected through hyperedge HE-4 (induced by source paper "efficient non oblivious randomized reduction for risk minimization with improved excess risk guarantee"):
  with members:
    - "optimal approximate matrix product in terms of stable rank"
    - "theory of dual sparse regularized randomized reduction"
    - "randomized sketches of convex programs with sharp guarantees"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "improved matrix algorithms via the subsampled randomized hadamard transform"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "sharp analysis of low rank kernel matrix approximations"
    - "is margin preserved after random projection"

Connected through hyperedge HE-5 (induced by source paper "the singular value decomposition applications and beyond"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "towards more efficient symmetric matrix sketching and the cur matrix decomposition"
    - "improved analyses of the randomized power method and block lanczos method"
    - "optimal approximate matrix product in terms of stable rank"
    - "sketching as a tool for numerical linear algebra"
    - "the modified nystrom method theories algorithms and extension"
    - "efficient algorithms and error analysis for the modified nystrom method"
    - "optimal cur matrix decompositions"

Connected through hyperedge HE-6 (induced by source paper "cur algorithm for partially observed matrices"):
  with members:
    - "sketching as a tool for numerical linear algebra"
    - "optimal cur matrix decompositions"
    - "improving cur matrix decomposition and the nystr o m approximation via adaptive sampling"
    - "a scalable cur matrix decomposition algorithm lower time complexity and tighter bound"
    - "recovering low rank matrices from few coefficients in any basis"
    - "near optimal column based matrix reconstruction"
    - "high rank matrix completion and subspace clustering with missing data"
    - "cur from a sparse optimization viewpoint"

Connected through hyperedge HE-7 (induced by source paper "far field compression for fast kernel summation methods in high dimensions"):
  with members:
    - "sketching as a tool for numerical linear algebra"
    - "matrix coherence and the nystrom method"
    - "askit approximate skeletonization kernel independent treecode in high dimensions"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "improved matrix algorithms via the subsampled randomized hadamard transform"
    - "optimized m2l kernels for the chebyshev interpolation based fast multipole method"
    - "fast approximation of matrix coherence and statistical leverage"
    - "randomized algorithms for matrices and data"

Connected through hyperedge HE-8 (induced by source paper "spsd matrix approximation vis column selection theories algorithms and extensions"):
  with members:
    - "towards more efficient spsd matrix approximation and cur matrix decomposition"
    - "sketching as a tool for numerical linear algebra"
    - "optimal cur matrix decompositions"
    - "matrix coherence and the nystrom method"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "fast approximation of matrix coherence and statistical leverage"
    - "optimal column based low rank matrix reconstruction"
    - "near optimal column based matrix reconstruction"

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)