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: "spsd matrix approximation vis column selection theories algorithms and extensions"
  Abstract: Symmetric positive semidefinite (SPSD) matrix approximation is an important problem with applications in kernel methods. However, existing SPSD matrix approximation methods such as the Nystr\"om method only have weak error bounds. In this paper we conduct in-depth studies of an SPSD matrix approximation model and establish strong relative-error bounds. We call it the prototype model for it has more efficient and effective extensions, and some of its extensions have high scalability. Though the prototype model itself is not suitable for large-scale data, it is still useful to study its properties, on which the analysis of its extensions relies. #R##N#This paper offers novel theoretical analysis, efficient algorithms, and a highly accurate extension. First, we establish a lower error bound for the prototype model and improve the error bound of an existing column selection algorithm to match the lower bound. In this way, we obtain the first optimal column selection algorithm for the prototype model. We also prove that the prototype model is exact under certain conditions. Second, we develop a simple column selection algorithm with a provable error bound. Third, we propose a so-called spectral shifting model to make the approximation more accurate when the eigenvalues of the matrix decay slowly, and the improvement is theoretically quantified. The spectral shifting method can also be applied to improve other SPSD matrix approximation models.

Connected through hyperedge HE-1 (induced by source paper "fast generalized matrix regression with applications in machine learning"):
  with members:
    - "practical sketching algorithms for low rank matrix approximation"
    - "towards more efficient spsd matrix approximation and cur matrix decomposition"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"
    - "low rank approximation and regression in input sparsity time"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
    - "fast approximation of matrix coherence and statistical leverage"
    - "near optimal column based matrix reconstruction"

Connected through hyperedge HE-2 (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-3 (induced by source paper "sublinear time low rank approximation of positive semidefinite matrices"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "practical sketching algorithms for low rank matrix approximation"
    - "randomized single view algorithms for low rank matrix approximation"
    - "sharper bounds for regression and low rank approximation with regularization"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"
    - "recursive sampling for the nystr om method"
    - "input sparsity time low rank approximation via ridge leverage score sampling"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"

Connected through hyperedge HE-4 (induced by source paper "giant globally improved approximate newton method for distributed optimization"):
  with members:
    - "federated learning strategies for improving communication efficiency"
    - "federated multi task learning"
    - "cocoa a general framework for communication efficient distributed optimization"
    - "communication efficient learning of deep networks from decentralized data"
    - "a general distributed dual coordinate optimization framework for regularized loss minimization"
    - "federated optimization distributed machine learning for on device intelligence"
    - "on variance reduction in stochastic gradient descent and its asynchronous variants"
    - "mllib machine learning in apache spark"

Connected through hyperedge HE-5 (induced by source paper "robust frequent directions with application in online learning"):
  with members:
    - "introduction to online convex optimization"
    - "efficient second order online learning by sketching"
    - "co occuring directions sketching for approximate matrix multiply"
    - "improved practical matrix sketching with guarantees"
    - "frequent directions simple and deterministic matrix sketching"
    - "optimizing neural networks with kronecker factored approximate curvature"
    - "sketching as a tool for numerical linear algebra"
    - "provable deterministic leverage score sampling"

Connected through hyperedge HE-6 (induced by source paper "a practical guide to randomized matrix computations with matlab implementations"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "towards more efficient spsd matrix approximation and cur matrix decomposition"
    - "communication optimal distributed principal component analysis in the column partition model"
    - "kernel interpolation for scalable structured gaussian processes kiss gp"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "optimal principal component analysis in distributed and streaming models"
    - "sketching as a tool for numerical linear algebra"
    - "the modified nystrom method theories algorithms and extension"

Connected through hyperedge HE-7 (induced by source paper "towards more efficient spsd matrix approximation and cur matrix decomposition"):
  with members:
    - "optimal approximate matrix product in terms of stable rank"
    - "sketching as a tool for numerical linear algebra"
    - "scalable kernel methods via doubly stochastic gradients"
    - "optimal cur matrix decompositions"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "low rank approximation and regression in input sparsity time"
    - "multi scale link prediction"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"

Connected through hyperedge HE-8 (induced by source paper "a simple approach to optimal cur decomposition"):
  with members:
    - "towards more efficient spsd matrix approximation and cur matrix decomposition"
    - "optimal approximate matrix product in terms of stable rank"
    - "optimal cur matrix decompositions"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "low rank approximation and regression in input sparsity time"
    - "fast approximation of matrix coherence and statistical leverage"
    - "near optimal column based matrix reconstruction"
    - "relative error cur matrix decompositions"

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)