human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "faster subset selection for matrices and applications"):
  Abstract: We study subset selection for matrices defined as follows: given a matrix $\matX \in \R^{n \times m}$ ($m > n$) and an oversampling parameter $k$ ($n \le k \le m$), select a subset of $k$ columns from $\matX$ such that the pseudo-inverse of the subsampled matrix has as smallest norm as possible. In this work, we focus on the Frobenius and the spectral matrix norms. We describe several novel (deterministic and randomized) approximation algorithms for this problem with approximation bounds that are optimal up to constant factors. Additionally, we show that the combinatorial problem of finding a low-stretch spanning tree in an undirected graph corresponds to subset selection, and discuss various implications of this reduction.

Includes members:
  Member "deterministic feature selection for k means clustering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "subset selection for matrices with fixed blocks")
  Member "a matrix hyperbolic cosine algorithm and applications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "spectral sparsification via bounded independence sampling")
  Member "on truncated svd like sparse solutions to least squares problems of arbitrary dimensions":
    also connected through other hyperedges:
      (none)
  Member "fast approximation of matrix coherence and statistical leverage":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sketching transformed matrices with applications to natural language processing")
  Member "optimal column based low rank matrix reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "improved guarantees and a multiple descent curve for the column subset selection problem and the nystr om method")
  Member "topics in matrix sampling algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "near optimal coresets for least squares regression")
  Member "near optimal column based matrix reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "good feature matching towards accurate robust vo vslam with low latency")
  Member "exponential inapproximability of selecting a maximum volume sub matrix":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "batch active learning using determinantal point processes")

Question: Please tell me which class the source paper 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)