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 "fully dynamic spectral vertex sparsifiers and applications"):
  Abstract: We study \emph{dynamic} algorithms for maintaining spectral vertex sparsifiers of graphs with respect to a set of terminals $T$ of our choice. Such objects preserve pairwise resistances, solutions to systems of linear equations, and energy of electrical flows between the terminals in $T$. We give a data structure that supports insertions and deletions of edges, and terminal additions, all in sublinear time. Our result is then applied to the following problems. #R##N#(1) A data structure for maintaining solutions to Laplacian systems $\mathbf{L} \mathbf{x} = \mathbf{b}$, where $\mathbf{L}$ is the Laplacian matrix and $\mathbf{b}$ is a demand vector. For a bounded degree, unweighted graph, we support modifications to both $\mathbf{L}$ and $\mathbf{b}$ while providing access to $\epsilon$-approximations to the energy of routing an electrical flow with demand $\mathbf{b}$, as well as query access to entries of a vector $\tilde{\mathbf{x}}$ such that $\left\lVert \tilde{\mathbf{x}}-\mathbf{L}^{\dagger} \mathbf{b} \right\rVert_{\mathbf{L}} \leq \epsilon \left\lVert \mathbf{L}^{\dagger} \mathbf{b} \right\rVert_{\mathbf{L}}$ in $\tilde{O}(n^{11/12}\epsilon^{-5})$ expected amortized update and query time. #R##N#(2) A data structure for maintaining All-Pairs Effective Resistance. For an intermixed sequence of edge insertions, deletions, and resistance queries, our data structure returns $(1 \pm \epsilon)$-approximation to all the resistance queries against an oblivious adversary with high probability. Its expected amortized update and query times are $\tilde{O}(\min(m^{3/4},n^{5/6} \epsilon^{-2}) \epsilon^{-4})$ on an unweighted graph, and $\tilde{O}(n^{5/6}\epsilon^{-6})$ on weighted graphs. #R##N#These results represent the first data structures for maintaining key primitives from the Laplacian paradigm for graph algorithms in sublinear time without assumptions on the underlying graph topologies.

Includes members:
  Member "dynamic low stretch trees via dynamic low diameter decompositions":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism")
  Member "graph reduction by local variation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "kirchhoff index as a measure of edge centrality in weighted networks nearly linear time algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "current flow group closeness centrality for complex networks")
  Member "dynamic effective resistances and approximate schur complement on separable graphs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "vertex sparsifiers for c edge connectivity")
  Member "on solving linear systems in sublinear time":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "quantum inspired sublinear classical algorithms for solving low rank linear systems")
  Member "spectrally approximating large graphs with smaller graphs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "an almost linear time algorithm for uniform random spanning tree generation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sketching transformed matrices with applications to natural language processing")
  Member "density independent algorithms for sparsifying k step random walks":
    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)