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 "sampling random spanning trees faster than matrix multiplication"):
  Abstract: We present an algorithm that, with high probability, generates a random spanning tree from an edge-weighted undirected graph in $\tilde{O}(n^{5/3 }m^{1/3})$ time (The $\tilde{O}(\cdot)$ notation hides $\operatorname{polylog}(n)$ factors). The tree is sampled from a distribution where the probability of each tree is proportional to the product of its edge weights. This improves upon the previous best algorithm due to Colbourn et al. that runs in matrix multiplication time, $O(n^\omega)$. For the special case of unweighted graphs, this improves upon the best previously known running time of $\tilde{O}(\min\{n^{\omega},m\sqrt{n},m^{4/3}\})$ for $m \gg n^{7/4}$ (Colbourn et al. '96, Kelner-Madry '09, Madry et al. '15). #R##N#The effective resistance metric is essential to our algorithm, as in the work of Madry et al., but we eschew determinant-based and random walk-based techniques used by previous algorithms. Instead, our algorithm is based on Gaussian elimination, and the fact that effective resistance is preserved in the graph resulting from eliminating a subset of vertices (called a Schur complement). As part of our algorithm, we show how to compute $\epsilon$-approximate effective resistances for a set $S$ of vertex pairs via approximate Schur complements in $\tilde{O}(m+(n + |S|)\epsilon^{-2})$ time, without using the Johnson-Lindenstrauss lemma which requires $\tilde{O}( \min\{(m + |S|)\epsilon^{-2}, m+n\epsilon^{-4} +|S|\epsilon^{-2}\})$ time. We combine this approximation procedure with an error correction procedure for handing edges where our estimate isn't sufficiently accurate.

Includes members:
  Member "approximate gaussian elimination for laplacians fast sparse and simple":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond")
  Member "sparsified cholesky and multigrid solvers for connection laplacians":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond")
  Member "fast generation of random spanning trees and the effective resistance metric":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a spectral approach to network design")
  Member "efficient accelerated coordinate descent methods and faster algorithms for solving linear systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "spectral sparsification via bounded independence sampling")
  Member "a simple combinatorial algorithm for solving sdd systems in nearly linear time":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "circulation control for faster minimum cost flow in unit capacity graphs")
  Member "faster spectral sparsification and numerical algorithms for sdd matrices":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fully dynamic spectral vertex sparsifiers and applications")
  Member "a nearly mlogn time solver for sdd linear systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "spectral sparsification via bounded independence sampling")
  Member "faster generation of random spanning trees":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dynamic streaming spectral sparsification in nearly linear time and space")

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)