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 spectral sparsification and numerical algorithms for sdd matrices"):
  Abstract: We study algorithms for spectral graph sparsification. The input is a graph $G$ with $n$ vertices and $m$ edges, and the output is a sparse graph $\tilde{G}$ that approximates $G$ in an algebraic sense. Concretely, for all vectors $x$ and any $\epsilon>0$, $\tilde{G}$ satisfies $$ (1-\epsilon) x^T L_G x \leq x^T L_{\tilde{G}} x \leq (1+\epsilon) x^T L_G x, $$ where $L_G$ and $L_{\tilde{G}}$ are the Laplacians of $G$ and $\tilde{G}$ respectively. We show that the fastest known algorithm for computing a sparsifier with $O(n\log n/\epsilon^2)$ edges can actually run in $\tilde{O}(m\log^2 n)$ time, an $O(\log n)$ factor faster than before. We also present faster sparsification algorithms for slightly dense graphs. Specifically, we give an algorithm that runs in $\tilde{O}(m\log n)$ time and generates a sparsifier with $\tilde{O}(n\log^3{n}/\epsilon^2)$ edges. This implies that a sparsifier with $O(n\log n/\epsilon^2)$ edges can be computed in $\tilde{O}(m\log n)$ time for graphs with more than $O(n\log^4 n)$ edges. We also give an $\tilde{O}(m)$ time algorithm for graphs with more than $n\log^5 n (\log \log n)^3$ edges of polynomially bounded weights, and an $O(m)$ algorithm for unweighted graphs with more than $n\log^8 n (\log \log n)^3 $ edges and $n\log^{10} n (\log \log n)^5$ edges in the weighted case. The improved sparsification algorithms are employed to accelerate linear system solvers and algorithms for computing fundamental eigenvectors of slightly dense SDD matrices.

Includes members:
  Member "simple parallel and distributed algorithms for spectral graph sparsification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "slim graph practical lossy graph compression for approximate graph processing storage and analytics")
  Member "iterative row sampling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sketching transformed matrices with applications to natural language processing")
  Member "a nearly mlogn time solver for sdd linear systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "spectral sparsification via bounded independence sampling")
  Member "approaching optimality for solving sdd systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "solving linear programs with sqrt rank linear system solves")
  Member "a note on preconditioning by low stretch spanning trees":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "dynamic low stretch trees via dynamic low diameter decompositions")
  Member "spectral sparsification of graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "graph sparsification by effective resistances":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "nearly tight low stretch spanning trees":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "2 node connectivity network design")

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)