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 new bounds and distributed algorithms"
  Abstract: The problem of column subset selection has recently attracted a large body of research, with feature selection serving as one obvious and important application. Among the techniques that have been applied to solve this problem, the greedy algorithm has been shown to be quite effective in practice. However, theoretical guarantees on its performance have not been explored thoroughly, especially in a distributed setting. In this paper, we study the greedy algorithm for the column subset selection problem from a theoretical and empirical perspective and show its effectiveness in a distributed setting. In particular, we provide an improved approximation guarantee for the greedy algorithm which we show is tight up to a constant factor, and present the first distributed implementation with provable approximation factors. We use the idea of randomized composable core-sets, developed recently in the context of submodular maximization. Finally, we validate the effectiveness of this distributed algorithm via an empirical study.

Connected through hyperedge HE-1 (induced by source paper "approximating spectral clustering via sampling a review"):
  with members:
    - "optimized deformed laplacian for spectrum based community detection in sparse heterogeneous graphs"
    - "revisiting the bethe hessian improved community detection in sparse heterogeneous graphs"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "graph reduction by local variation"
    - "scalable spectral clustering using random binning features"
    - "graph reduction with spectral and cut guarantees"
    - "spectrally approximating large graphs with smaller graphs"
    - "ann benchmarks a benchmarking tool for approximate nearest neighbor algorithms"

Connected through hyperedge HE-2 (induced by source paper "improved guarantees and a multiple descent curve for the column subset selection problem and the nystr om method"):
  with members:
    - "two models of double descent for weak features"
    - "exact sampling of determinantal point processes with sublinear time preprocessing"
    - "bayesian experimental design using regularized determinantal point processes"
    - "exact expressions for double descent and implicit regularization via surrogate random design"
    - "minimax experimental design bridging the gap between statistical and worst case approaches to least squares regression"
    - "fast determinantal point processes via distortion free intermediate sampling"
    - "reverse iterative volume sampling for linear regression"
    - "monte carlo markov chain algorithms for sampling strongly rayleigh distributions and determinantal point processes"

Connected through hyperedge HE-3 (induced by source paper "non submodular function maximization subject to a matroid constraint with applications"):
  with members:
    - "submodular maximization beyond non negativity guarantees fast algorithms and applications"
    - "steering social activity a stochastic optimal control point of view"
    - "equity of attention amortizing individual fairness in rankings"
    - "deep reinforcement learning of marked temporal point processes"
    - "fairness of exposure in rankings"
    - "combinatorial penalties which structures are preserved by convex relaxations"
    - "weakly submodular maximization beyond cardinality constraints does randomization help greedy"
    - "gradient methods for submodular maximization"

Connected through hyperedge HE-4 (induced by source paper "regularized greedy column subset selection"):
  with members:
    - "unsupervised feature selection with adaptive structure learning"
    - "optimal column based low rank matrix reconstruction"
    - "near optimal column based matrix reconstruction"
    - "efficient volume sampling for row column subset selection"
    - "an improved approximation algorithm for the column subset selection problem"

Connected through hyperedge HE-5 (induced by source paper "guarantees for greedy maximization of non submodular functions with applications"):
  with members:
    - "guaranteed non convex optimization submodular maximization over continuous domains"
    - "maximizing a monotone submodular function with a bounded curvature under a knapsack constraint"
    - "weakly submodular functions"
    - "on iterative hard thresholding methods for high dimensional m estimation"
    - "curvature and optimal algorithms for learning and minimizing submodular functions"
    - "optimal approximation for submodular and supermodular optimization with bounded curvature"
    - "algorithms for approximate minimization of the difference between submodular functions with applications"
    - "a submodular supermodular procedure with applications to discriminative structure learning"

Connected through hyperedge HE-6 (induced by source paper "compressive k means"):
  with members:
    - "sketching for large scale learning of mixture models"
    - "compressive spectral clustering"
    - "flexible multilayer sparse approximations of matrices and applications"
    - "accelerated spectral clustering using graph filtering of random signals"
    - "exact solutions to super resolution on semi algebraic domains in higher dimensions"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "a unified framework for approximating and clustering data"
    - "random projections for k means clustering"

Connected through hyperedge HE-7 (induced by source paper "distributed coverage maximization via sketching"):
  with members:
    - "almost optimal streaming algorithms for coverage problems"
    - "the power of randomization distributed submodular maximization on massive datasets"
    - "randomized composable core sets for distributed submodular maximization"
    - "a new framework for distributed submodular maximization"
    - "lazier than lazy greedy"
    - "parallel algorithms for geometric graph problems"

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)