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: "dimensionality reduction of massive sparse datasets using coresets"
  Abstract: In this paper we present a practical solution with performance guarantees to the problem of dimensionality reduction for very large scale sparse matrices. We show applications of our approach to computing the low rank approximation (reduced SVD) of such matrices. Our solution uses coresets, which is a subset of $O(k/\eps^2)$ scaled rows from the $n\times d$ input matrix, that approximates the sub of squared distances from its rows to every $k$-dimensional subspace in $\REAL^d$, up to a factor of $1\pm\eps$. An open theoretical problem has been whether we can compute such a coreset that is independent of the input matrix and also a weighted subset of its rows. %An open practical problem has been whether we can compute a non-trivial approximation to the reduced SVD of very large databases such as the Wikipedia document-term matrix in a reasonable time. We answer this question affirmatively. % and demonstrate an algorithm that efficiently computes a low rank approximation of the entire English Wikipedia. Our main technical result is a novel technique for deterministic coreset construction that is based on a reduction to the problem of $\ell_2$ approximation for item frequencies.

Connected through hyperedge HE-1 (induced by source paper "sparse coresets for svd on infinite streams"):
  with members:
    - "tight sensitivity bounds for smaller coresets"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "optimal approximate matrix product in terms of stable rank"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "fast approximation of matrix coherence and statistical leverage"
    - "a unified framework for approximating and clustering data"

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"
    - "frequent directions simple and deterministic matrix sketching"
    - "optimal principal component analysis in distributed and streaming models"
    - "improved bounds on sample size for implicit matrix trace estimators"

Connected through hyperedge HE-3 (induced by source paper "robust coreset construction for distributed machine learning"):
  with members:
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "federated learning strategies for improving communication efficiency"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "federated multi task learning"
    - "coresets and sketches"
    - "new frameworks for offline and streaming coreset constructions"
    - "communication efficient learning of deep networks from decentralized data"
    - "geometric median in nearly linear time"

Connected through hyperedge HE-4 (induced by source paper "real time eeg classification via coresets for bci applications"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "coresets and sketches"
    - "geometric median in nearly linear time"
    - "randomized composable core sets for distributed submodular maximization"
    - "epsilon kernel coresets for stochastic points"
    - "on the sensitivity of shape fitting problems"
    - "simple and deterministic matrix sketching"
    - "a unified framework for approximating and clustering data"

Connected through hyperedge HE-5 (induced by source paper "fast and accurate least mean squares solvers"):
  with members:
    - "coresets and sketches"
    - "coresets for kinematic data from theorems to real time systems"

Connected through hyperedge HE-6 (induced by source paper "greedy frank wolfe algorithm for exemplar selection"):
  with members:
    - "efficient solvers for sparse subspace clustering"
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "emnist an extension of mnist to handwritten letters"
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "sparse subspace clustering algorithm theory and applications"
    - "an improved approximation algorithm for the column subset selection problem"
    - "relative error cur matrix decompositions"

Connected through hyperedge HE-7 (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"
    - "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"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"

Connected through hyperedge HE-8 (induced by source paper "fixed rank approximation of a positive semidefinite matrix from streaming data"):
  with members:
    - "sublinear time low rank approximation of positive semidefinite matrices"
    - "randomized single view algorithms for low rank matrix approximation"
    - "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm"
    - "frequent directions simple and deterministic matrix sketching"
    - "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"

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)