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 "a framework for analyzing resparsification algorithms"):
  Abstract: A spectral sparsifier of a graph $G$ is a sparser graph $H$ that approximately preserves the quadratic form of $G$, i.e. for all vectors $x$, $x^T L_G x \approx x^T L_H x$, where $L_G$ and $L_H$ denote the respective graph Laplacians. Spectral sparsifiers generalize cut sparsifiers, and have found many applications in designing graph algorithms. In recent years, there has been interest in computing spectral sparsifiers in semi-streaming and dynamic settings. Natural algorithms in these settings often involve repeated sparsification of a graph, and accumulation of errors across these steps. We present a framework for analyzing algorithms that perform repeated sparsifications that only incur error corresponding to a single sparsification step, leading to better results for many resparsification-based algorithms. As an application, we show how to maintain a spectral sparsifier in the semi-streaming setting: We present a simple algorithm that, for a graph $G$ on $n$ vertices and $m$ edges, computes a spectral sparsifier of $G$ with $O(n \log n)$ edges in a single pass over $G$, using only $O(n \log n)$ space, and $O(m \log^2 n)$ total time. This improves on previous best semi-streaming algorithms for both spectral and cut sparsifiers by a factor of $\log{n}$ in both space and runtime. The algorithm extends to semi-streaming row sampling for general PSD matrices. We also use our framework to combine a spectral sparsification algorithm by Koutis with improved spanner constructions to give a parallel algorithm for constructing $O(n\log^2{n}\log\log{n})$ sized spectral sparsifiers in $O(m\log^2{n}\log\log{n})$ time. This is the best known combinatorial graph sparsification algorithm.The size of the sparsifiers is only a factor $\log{n}\log\log{n}$ more than ones produced by numerical routines.

Includes members:
  Member "single and multi level network sparsification by algebraic distance":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fobe and hobe first and high order bipartite embeddings")
  Member "online row sampling":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a deterministic streaming sketch for ridge regression")
  Member "on sketching quadratic forms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "practice of streaming and dynamic graphs concepts models systems and parallelism")
  Member "simple parallel and distributed algorithms for spectral graph sparsification":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "slim graph practical lossy graph compression for approximate graph processing storage and analytics")
  Member "parallel graph decompositions using random shifts":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "scattering and sparse partitions and their applications")
  Member "improved parallel algorithms for spanners and hopsets":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient and simple algorithms for fault tolerant spanners")
  Member "faster spectral sparsification and numerical algorithms for sdd matrices":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "fully dynamic spectral vertex sparsifiers and applications")
  Member "approximating the exponential the lanczos method and an tilde o m time spectral algorithm for balanced separator":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond")

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)