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: "greedy column subset selection for large scale data sets"
  Abstract: In today's information systems, the availability of massive amounts of data necessitates the development of fast and accurate algorithms to summarize these data and represent them in a succinct format. One crucial problem in big data analytics is the selection of representative instances from large and massively-distributed data, which is formally known as the Column Subset Selection (CSS) problem. The solution to this problem enables data analysts to understand the insights of the data and explore its hidden structure. The selected instances can also be used for data preprocessing tasks such as learning a low-dimensional embedding of the data points or computing a low-rank approximation of the corresponding matrix. This paper presents a fast and accurate greedy algorithm for large-scale column subset selection. The algorithm minimizes an objective function which measures the reconstruction error of the data matrix based on the subset of selected columns. The paper first presents a centralized greedy algorithm for column subset selection which depends on a novel recursive formula for calculating the reconstruction error of the data matrix. The paper then presents a MapReduce algorithm which selects a few representative columns from a matrix whose columns are massively distributed across several commodity machines. The algorithm first learns a concise representation of all columns using random projection, and it then solves a generalized column subset selection problem at each machine in which a subset of columns are selected from the sub-matrix on that machine such that the reconstruction error of the concise representation is minimized. The paper demonstrates the effectiveness and efficiency of the proposed algorithm through an empirical evaluation on benchmark data sets.

Connected through hyperedge HE-1 (induced by source paper "progressive stochastic greedy sparse reconstruction and support selection"):
  with members:
    - "scalable sparse subspace clustering by orthogonal matching pursuit"
    - "lazier than lazy greedy"
    - "a remark on the restricted isometry property in orthogonal matching pursuit"
    - "optimal column based low rank matrix reconstruction"
    - "near optimal column based matrix reconstruction"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-2 (induced by source paper "a multi criteria approach for fast and outlier aware representative selection from manifolds"):
  with members:
    - "salient object detection a discriminative regional feature integration approach"
    - "dissimilarity based sparse subset selection"
    - "robust multiple manifolds structure learning"
    - "randomized algorithms for matrices and data"
    - "robust principal component analysis"
    - "an improved approximation algorithm for the column subset selection problem"

Connected through hyperedge HE-3 (induced by source paper "iterative projection and matching finding structure preserving representatives and its application to computer vision"):
  with members:
    - "cr gan learning complete representations for multi view generation"
    - "proportional volume sampling and approximation algorithms for a optimal design"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "the kinetics human action video dataset"
    - "deep bayesian active learning with image data"
    - "subsampling for ridge regression via regularized volume sampling"
    - "robust and scalable column row sampling from corrupted big data"

Connected through hyperedge HE-4 (induced by source paper "spatial random sampling a structure preserving data sketching tool"):
  with members:
    - "high dimensional low rank plus sparse matrix decomposition"
    - "innovation pursuit a new approach to subspace clustering"
    - "robust and scalable column row sampling from corrupted big data"
    - "column selection via adaptive sampling"
    - "provable deterministic leverage score sampling"
    - "optimal column based low rank matrix reconstruction"
    - "a sparse johnson lindenstrauss transform"
    - "random projections for k means clustering"

Connected through hyperedge HE-5 (induced by source paper "greedy column subset selection new bounds and distributed algorithms"):
  with members:
    - "turning big data into tiny data constant size coresets for k means pca and projective clustering"
    - "dimensionality reduction of massive sparse datasets using coresets"
    - "communication optimal distributed principal component analysis in the column partition model"
    - "the power of randomization distributed submodular maximization on massive datasets"
    - "randomized composable core sets for distributed submodular maximization"
    - "greedy minimization of weakly supermodular set functions"
    - "lazier than lazy greedy"
    - "dimensionality reduction for k means clustering and low rank approximation"

Connected through hyperedge HE-6 (induced by source paper "supervised dictionary learning and sparse representation a review"):
  with members:
    - "kernelized supervised dictionary learning"
    - "an improved approximation algorithm for the column subset selection problem"
    - "nonnegative matrix factorization via rank one downdate"
    - "non negative matrix factorization with sparseness constraints"

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)