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: "principal component projection without principal component analysis"
  Abstract: We show how to efficiently project a vector onto the top principal components of a matrix, without explicitly computing these components. Specifically, we introduce an iterative algorithm that provably computes the projection using few calls to any black-box routine for ridge regression. #R##N#By avoiding explicit principal component analysis (PCA), our algorithm is the first with no runtime dependence on the number of top principal components. We show that it can be used to give a fast iterative method for the popular principal component regression problem, giving the first major runtime improvement over the naive method of combining PCA with regression. #R##N#To achieve our results, we first observe that ridge regression can be used to obtain a "smooth projection" onto the top principal components. We then sharpen this approximation to true projection using a low-degree polynomial approximation to the matrix step function. Step function approximation is a topic of long-term interest in scientific computing. We extend prior theory by constructing polynomials with simple iterative structure and rigorously analyzing their behavior under limited precision.

Connected through hyperedge HE-1 (induced by source paper "over parameterized adversarial training an analysis overcoming the curse of dimensionality"):
  with members:
    - "quadratic suffices for over parametrization via matrix chernoff bound"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "introduction to online convex optimization"
    - "on exact computation with an infinitely wide neural net"
    - "fine grained analysis of optimization and generalization for overparameterized two layer neural networks"
    - "learning and generalization in overparameterized neural networks going beyond two layers"
    - "gradient descent finds global minima of deep neural networks"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"

Connected through hyperedge HE-2 (induced by source paper "principal component projection and regression in nearly linear time through asymmetric svrg"):
  with members:
    - "stability of the lanczos method for matrix function approximation"
    - "stochastic variance reduction methods for saddle point problems"
    - "lazysvd even faster svd decomposition yet without agonizing pain"
    - "faster eigenvector computation via shift and invert preconditioning"
    - "low rank approximation and regression in input sparsity time"

Connected through hyperedge HE-3 (induced by source paper "a universal sampling method for reconstructing signals with simple fourier transforms"):
  with members:
    - "relating leverage scores and density using regularized christoffel functions"
    - "random fourier features for kernel ridge regression approximation bounds and statistical guarantees"
    - "sublinear time low rank approximation of positive semidefinite matrices"
    - "active regression via linear sample sparsification"
    - "fourier sparse interpolation without a frequency gap"
    - "a robust sparse fourier transform in the continuous setting"
    - "recursive sampling for the nystr om method"
    - "input sparsity time low rank approximation via ridge leverage score sampling"

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"
    - "solving ridge regression using sketched preconditioned svrg"
    - "faster eigenvector computation via shift and invert preconditioning"

Connected through hyperedge HE-5 (induced by source paper "stability of the lanczos method for matrix function approximation"):
  with members:
    - "spectrum approximation beyond fast matrix multiplication algorithms and hardness"
    - "lazysvd even faster svd decomposition yet without agonizing pain"
    - "faster algorithms for computing the stationary distribution simulating random walks and more"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "large scale log determinant computation through stochastic chebyshev expansions"
    - "the noisy power method a meta algorithm with applications"
    - "approximating the exponential the lanczos method and an tilde o m time spectral algorithm for balanced separator"
    - "a note on preconditioning by low stretch spanning trees"

Connected through hyperedge HE-6 (induced by source paper "the zipml framework for training models with end to end low precision the cans the cannots and a little bit of deep learning"):
  with members:
    - "dorefa net training low bitwidth convolutional neural networks with low bitwidth gradients"
    - "qsgd communication efficient sgd via gradient quantization and encoding"
    - "ternary weight networks"
    - "bitwise neural networks"
    - "convolutional neural networks using logarithmic data representation"
    - "quantized neural networks training neural networks with low precision weights and activations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "fixed point quantization of deep convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "globally convergent stochastic optimization for canonical correlation analysis"):
  with members:
    - "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis"
    - "robust shift and invert preconditioning faster and more sample efficient algorithms for eigenvector computation"
    - "the fast convergence of incremental pca"
    - "large scale approximate kernel canonical correlation analysis"
    - "scale up nonlinear component analysis with doubly stochastic gradients"
    - "stochastic optimization for deep cca via nonlinear orthogonal iterations"
    - "a stochastic pca and svd algorithm with an exponential convergence rate"
    - "adaptive canonical correlation analysis based on matrix manifolds"

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.DS(Data Structures and Algorithms)