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: "sharp analysis of low rank kernel matrix approximations"
  Abstract: We consider supervised learning problems within the positive-definite kernel framework, such as kernel ridge regression, kernel logistic regression or the support vector machine. With kernels leading to infinite-dimensional feature spaces, a common practical limiting difficulty is the necessity of computing the kernel matrix, which most frequently leads to algorithms with running time at least quadratic in the number of observations n, i.e., O(n^2). Low-rank approximations of the kernel matrix are often considered as they allow the reduction of running time complexities to O(p^2 n), where p is the rank of the approximation. The practicality of such methods thus depends on the required rank p. In this paper, we show that in the context of kernel ridge regression, for approximations based on a random subset of columns of the original kernel matrix, the rank p may be chosen to be linear in the degrees of freedom associated with the problem, a quantity which is classically used in the statistical analysis of such methods, and is often seen as the implicit number of parameters of non-parametric estimators. This result enables simple algorithms that have sub-quadratic running time complexity, but provably exhibit the same predictive performance than existing algorithms, for any given problem instance, and not only for worst-case situations.

Connected through hyperedge HE-1 (induced by source paper "diversity sampling is an implicit regularization for kernel methods"):
  with members:
    - "hidden stratification causes clinically meaningful failures in machine learning for medical imaging"
    - "does learning require memorization a short tale about a long tail"
    - "scalable kernel k means clustering with nystrom approximation relative error bounds"

Connected through hyperedge HE-2 (induced by source paper "on the effectiveness of richardson extrapolation in machine learning"):
  with members:
    - "breaking the curse of dimensionality with convex neural networks"
    - "super samples from kernel herding"
    - "optimization with sparsity inducing penalties"

Connected through hyperedge HE-3 (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-4 (induced by source paper "random fourier features via fast surrogate leverage weighted sampling"):
  with members:
    - "nystrom landmark sampling and regularized christoffel functions"
    - "but how does it work in theory linear svm with random features"
    - "low precision random fourier features for memory constrained kernel approximation"
    - "quadrature based features for kernel approximation"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "gaussian quadrature for kernel features"
    - "orthogonal random features"
    - "input sparsity time low rank approximation via ridge leverage score sampling"

Connected through hyperedge HE-5 (induced by source paper "on sampling random features from empirical leverage scores implementation and theoretical guarantees"):
  with members:
    - "but how does it work in theory linear svm with random features"
    - "learning bounds for greedy approximation with explicit feature maps from multiple kernels"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "data driven random fourier features using stein effect"
    - "orthogonal random features"
    - "on the equivalence between kernel quadrature rules and random feature expansions"
    - "fastfood approximate kernel expansions in loglinear time"
    - "random feature maps for dot product kernels"

Connected through hyperedge HE-6 (induced by source paper "tight kernel query complexity of kernel ridge regression and kernel k means clustering"):
  with members:
    - "on the fine grained complexity of empirical risk minimization kernel methods and neural networks"
    - "sublinear time low rank approximation of positive semidefinite matrices"
    - "on learning mixtures of well separated gaussians"
    - "recursive sampling for the nystr om method"
    - "on the complexity of learning with kernels"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "adaptively learning the crowd kernel"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-7 (induced by source paper "nystrom landmark sampling and regularized christoffel functions"):
  with members:
    - "approximation beats concentration an approximation view on inference with smooth radial kernels"
    - "relating leverage scores and density using regularized christoffel functions"
    - "online row sampling"
    - "efficient sampling for k determinantal point processes"
    - "column selection via adaptive sampling"
    - "provable deterministic leverage score sampling"
    - "revisiting the nystrom method for improved large scale machine learning"

Connected through hyperedge HE-8 (induced by source paper "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"):
  with members:
    - "constant time predictive distributions for gaussian processes"
    - "averaging weights leads to wider optima and better generalization"
    - "product kernel interpolation for scalable gaussian processes"
    - "improved stochastic trace estimation using mutually unbiased bases"
    - "entropy sgd biasing gradient descent into wide valleys"
    - "on large batch training for deep learning generalization gap and sharp minima"
    - "thoughts on massively scalable gaussian processes"
    - "kernel interpolation for scalable structured gaussian processes kiss gp"

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)