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: "determinant preserving sparsification of sddm matrices with applications to counting and sampling spanning trees"
  Abstract: We show variants of spectral sparsification routines can preserve the total spanning tree counts of graphs, which by Kirchhoff's matrix-tree theorem, is equivalent to determinant of a graph Laplacian minor, or equivalently, of any SDDM matrix. Our analyses utilizes this combinatorial connection to bridge between statistical leverage scores / effective resistances and the analysis of random graphs by [Janson, Combinatorics, Probability and Computing `94]. This leads to a routine that in quadratic time, sparsifies a graph down to about $n^{1.5}$ edges in ways that preserve both the determinant and the distribution of spanning trees (provided the sparsified graph is viewed as a random object). Extending this algorithm to work with Schur complements and approximate Choleksy factorizations leads to algorithms for counting and sampling spanning trees which are nearly optimal for dense graphs. #R##N#We give an algorithm that computes a $(1 \pm \delta)$ approximation to the determinant of any SDDM matrix with constant probability in about $n^2 \delta^{-2}$ time. This is the first routine for graphs that outperforms general-purpose routines for computing determinants of arbitrary matrices. We also give an algorithm that generates in about $n^2 \delta^{-2}$ time a spanning tree of a weighted undirected graph from a distribution with total variation distance of $\delta$ from the $w$-uniform distribution .

Connected through hyperedge HE-1 (induced by source paper "fully dynamic spectral vertex sparsifiers and applications"):
  with members:
    - "dynamic low stretch trees via dynamic low diameter decompositions"
    - "graph reduction by local variation"
    - "kirchhoff index as a measure of edge centrality in weighted networks nearly linear time algorithms"
    - "dynamic effective resistances and approximate schur complement on separable graphs"
    - "on solving linear systems in sublinear time"
    - "spectrally approximating large graphs with smaller graphs"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "density independent algorithms for sparsifying k step random walks"

Connected through hyperedge HE-2 (induced by source paper "maximizing the number of spanning trees in a connected graph"):
  with members:
    - "current flow group closeness centrality for complex networks"
    - "fully dynamic spectral vertex sparsifiers and applications"
    - "kirchhoff index as a measure of edge centrality in weighted networks nearly linear time algorithms"
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "a nearly linear time algorithm for submodular maximization with a knapsack constraint"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "subdeterminant maximization via nonconvex relaxations and anti concentration"
    - "sampling random spanning trees faster than matrix multiplication"

Connected through hyperedge HE-3 (induced by source paper "spectral subspace sparsification"):
  with members:
    - "dynamic effective resistances and approximate schur complement on separable graphs"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "improved guarantees for vertex sparsification in planar graphs"
    - "sampling random spanning trees faster than matrix multiplication"
    - "graph minors for preserving terminal distances approximately lower and upper bounds"
    - "approximate gaussian elimination for laplacians fast sparse and simple"
    - "vertex sparsifiers new results from old techniques"
    - "approximate maximum flow on separable undirected graphs"

Connected through hyperedge HE-4 (induced by source paper "an almost linear time algorithm for uniform random spanning tree generation"):
  with members:
    - "localization of electrical flows"
    - "sampling random spanning trees faster than matrix multiplication"
    - "approximate gaussian elimination for laplacians fast sparse and simple"
    - "sparsified cholesky solvers for sdd linear systems"
    - "fast generation of random spanning trees and the effective resistance metric"
    - "an efficient parallel solver for sdd linear systems"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "graph sparsification by edge connectivity and random spanning trees"

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)