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 matrix algorithms via the subsampled randomized hadamard transform"
  Abstract: Several recent randomized linear algebra algorithms rely upon fast dimension reduction methods. A popular choice is the Subsampled Randomized Hadamard Transform (SRHT). In this article, we address the efficacy, in the Frobenius and spectral norms, of an SRHT-based low-rank matrix approximation technique introduced by Woolfe, Liberty, Rohklin, and Tygert. We establish a slightly better Frobenius norm error bound than currently available, and a much sharper spectral norm error bound (in the presence of reasonable decay of the singular values). Along the way, we produce several results on matrix operations with SRHTs (such as approximate matrix multiplication) that may be of independent interest. Our approach builds upon Tropp's in "Improved analysis of the Subsampled Randomized Hadamard Transform".

Connected through hyperedge HE-1 (induced by source paper "improved subsampled randomized hadamard transform for linear svm"):
  with members:
    - "faster neural network training with approximate tensor operations"
    - "fully understanding the hashing trick"
    - "provable deterministic leverage score sampling"
    - "recovering the optimal solution by dual random projection"
    - "low rank approximation and regression in input sparsity time"
    - "randomized algorithms for matrices and data"
    - "random projections for k means clustering"
    - "feature hashing for large scale multitask learning"

Connected through hyperedge HE-2 (induced by source paper "streaming low rank matrix approximation with an application to scientific simulation"):
  with members:
    - "low rank tucker approximation of a tensor from streaming data"
    - "practical sketching algorithms for low rank matrix approximation"
    - "randomized single view algorithms for low rank matrix approximation"
    - "fast and space optimal low rank factorization in the streaming model with application in differential privacy"
    - "parallel tensor compression for large scale scientific data"
    - "dimensionality reduction of massive sparse datasets using coresets"
    - "frequent directions simple and deterministic matrix sketching"
    - "optimal principal component analysis in distributed and streaming models"

Connected through hyperedge HE-3 (induced by source paper "learning based low rank approximations"):
  with members:
    - "a model for learned bloom filters and optimizing by sandwiching"
    - "learning to branch"
    - "hyperspectral image dataset for benchmarking on salient object detection"
    - "competitive caching with machine learned advice"
    - "the case for learned index structures"
    - "learning combinatorial optimization algorithms over graphs"
    - "global guarantees for enforcing deep generative priors by empirical risk"
    - "lazysvd even faster svd decomposition yet without agonizing pain"

Connected through hyperedge HE-4 (induced by source paper "low rank tucker approximation of a tensor from streaming data"):
  with members:
    - "streaming low rank matrix approximation with an application to scientific simulation"
    - "tthresh tensor compression for multidimensional visual data"
    - "a practical randomized cp tensor decomposition"
    - "sketching for kronecker product regression and p splines"
    - "practical sketching algorithms for low rank matrix approximation"
    - "parallel tensor compression for large scale scientific data"
    - "decomposition of big tensors with low multilinear rank"
    - "tensor decompositions a new concept in brain data analysis"

Connected through hyperedge HE-5 (induced by source paper "sketch based randomized algorithms for dynamic graph regression"):
  with members:
    - "on the theory of dynamic graph regression problem"
    - "energy efficient hadamard neural networks"
    - "node2vec scalable feature learning for networks"
    - "learning convolutional neural networks for graphs"
    - "revisiting semi supervised learning with graph embeddings"
    - "low rank approximation and regression in input sparsity time"
    - "faster least squares approximation"

Connected through hyperedge HE-6 (induced by source paper "an empirical evaluation of sketched svd and its application to leverage score ordering"):
  with members:
    - "seq2seq2sentiment multimodal sequence to sequence models for sentiment analysis"
    - "learning factorized multimodal representations"
    - "near optimal sketching of low rank tensor regression"
    - "sketching for kronecker product regression and p splines"
    - "practical and optimal lsh for angular distance"
    - "distributed kernel principal component analysis"
    - "speech recognition with deep recurrent neural networks"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"

Connected through hyperedge HE-7 (induced by source paper "randomized rank revealing uzv decomposition for low rank approximation of matrices"):
  with members:
    - "low rank approximation and regression in input sparsity time"
    - "robust principal component analysis"

Connected through hyperedge HE-8 (induced by source paper "practical sketching algorithms for low rank matrix approximation"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "randomized single view algorithms for low rank matrix approximation"
    - "dimensionality reduction of massive sparse datasets using coresets"
    - "optimal principal component analysis in distributed and streaming models"
    - "optimal approximate matrix product in terms of stable rank"
    - "sketching as a tool for numerical linear algebra"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "lower bounds for oblivious subspace embeddings"

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)