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: "low rank matrix valued chernoff bounds and approximate matrix multiplication"
  Abstract: In this paper we develop algorithms for approximating matrix multiplication with respect to the spectral norm. Let A\in{\RR^{n\times m}} and B\in\RR^{n \times p} be two matrices and \eps>0. We approximate the product A^\top B using two down-sampled sketches, \tilde{A}\in\RR^{t\times m} and \tilde{B}\in\RR^{t\times p}, where t\ll n such that \norm{\tilde{A}^\top \tilde{B} - A^\top B} \leq \eps \norm{A}\norm{B} with high probability. We use two different sampling procedures for constructing \tilde{A} and \tilde{B}; one of them is done by i.i.d. non-uniform sampling rows from A and B and the other is done by taking random linear combinations of their rows. We prove bounds that depend only on the intrinsic dimensionality of A and B, that is their rank and their stable rank; namely the squared ratio between their Frobenius and operator norm. For achieving bounds that depend on rank we employ standard tools from high-dimensional geometry such as concentration of measure arguments combined with elaborate \eps-net constructions. For bounds that depend on the smaller parameter of stable rank this technology itself seems weak. However, we show that in combination with a simple truncation argument is amenable to provide such bounds. To handle similar bounds for row sampling, we develop a novel matrix-valued Chernoff bound inequality which we call low rank matrix-valued Chernoff bound. Thanks to this inequality, we are able to give bounds that depend only on the stable rank of the input matrices...

Connected through hyperedge HE-1 (induced by source paper "on dimension free tail inequalities for sums of random matrices and applications"):
  with members:
    - "matrix infinitely divisible series tail inequalities and applications in optimization"
    - "recycling randomness with structure for sublinear time kernel expansions"
    - "an exploration of parameter redundancy in deep networks with circulant projections"
    - "a note on element wise matrix sparsification via a matrix valued bernstein inequality"
    - "circulant and toeplitz matrices in compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "multiplicative rank 1 approximation using length squared sampling"):
  with members:
    - "low rank matrix approximation in linear time"
    - "low rank approximation and regression in input sparsity time"
    - "faster least squares approximation"

Connected through hyperedge HE-3 (induced by source paper "when hashes met wedges a distributed algorithm for finding high similarity vectors"):
  with members:
    - "practical and optimal lsh for angular distance"
    - "diamond sampling for approximate maximum all pairs dot product mad search"
    - "the unified logging infrastructure for data analytics at twitter"
    - "complexity measures for map reduce and comparison to parallel computing"
    - "dimension independent similarity computation"
    - "compressed matrix multiplication"
    - "layered label propagation a multiresolution coordinate free ordering for compressing social networks"
    - "on sparse representations of linear operators and the approximation of matrix products"

Connected through hyperedge HE-4 (induced by source paper "fast and space optimal low rank factorization in the streaming model with application in differential privacy"):
  with members:
    - "optimal principal component analysis in distributed and streaming models"
    - "wishart mechanism for differentially private principal components analysis"
    - "differentially private linear algebra in the streaming model"
    - "randomness efficient fast johnson lindenstrauss transform with applications in differential privacy and compressed sensing"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "the noisy power method a meta algorithm with applications"
    - "low rank approximation and regression in input sparsity time"
    - "calibrating data to sensitivity in private data analysis"

Connected through hyperedge HE-5 (induced by source paper "structural convergence results for low rank approximations from block krylov spaces"):
  with members:
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "improved analyses of the randomized power method and block lanczos method"
    - "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"
    - "near optimal column based matrix reconstruction"
    - "faster least squares approximation"
    - "relative error cur matrix decompositions"

Connected through hyperedge HE-6 (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-7 (induced by source paper "optimal approximate matrix product in terms of stable rank"):
  with members:
    - "sketching as a tool for numerical linear algebra"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "improved distributed principal component analysis"
    - "uniform sampling for matrix approximation"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "lower bounds for oblivious subspace embeddings"
    - "low rank approximation and regression in input sparsity time"
    - "new probabilistic bounds on eigenvalues and eigenvectors of random kernel matrices"

Connected through hyperedge HE-8 (induced by source paper "fast spectral low rank matrix approximation"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "sketching as a tool for numerical linear algebra"
    - "lower bounds for oblivious subspace embeddings"
    - "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"
    - "fast matrix rank algorithms and applications"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"

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)