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 "near linear work parallel sdd solvers low diameter decomposition and low stretch subgraphs"):
  Abstract: We present the design and analysis of a near linear-work parallel algorithm for solving symmetric diagonally dominant (SDD) linear systems. On input of a SDD $n$-by-$n$ matrix $A$ with $m$ non-zero entries and a vector $b$, our algorithm computes a vector $\tilde{x}$ such that $\norm[A]{\tilde{x} - A^+b} \leq \vareps \cdot \norm[A]{A^+b}$ in $O(m\log^{O(1)}{n}\log{\frac1\epsilon})$ work and $O(m^{1/3+\theta}\log \frac1\epsilon)$ depth for any fixed $\theta > 0$. #R##N#The algorithm relies on a parallel algorithm for generating low-stretch spanning trees or spanning subgraphs. To this end, we first develop a parallel decomposition algorithm that in polylogarithmic depth and $\otilde(|E|)$ work, partitions a graph into components with polylogarithmic diameter such that only a small fraction of the original edges are between the components. This can be used to generate low-stretch spanning trees with average stretch $O(n^{\alpha})$ in $O(n^{1+\alpha})$ work and $O(n^{\alpha})$ depth. Alternatively, it can be used to generate spanning subgraphs with polylogarithmic average stretch in $\otilde(|E|)$ work and polylogarithmic depth. We apply this subgraph construction to derive a parallel linear system solver. By using this solver in known applications, our results imply improved parallel randomized algorithms for several problems, including single-source shortest paths, maximum flow, minimum-cost flow, and approximate maximum flow.

Includes members:
  Member "a nearly mlogn time solver for sdd linear systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spectral sparsification via bounded independence sampling")
  Member "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a spectral approach to network design")
  Member "faster approximate lossy generalized flow via interior point algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "faster divergence maximization for faster maximum flow")
  Member "graph sparsification by effective resistances":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (induced by source paper "2 node connectivity network design")
  Member "nearly linear time algorithms for preconditioning and solving symmetric diagonally dominant linear systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "speeding up linear programming using randomized linear algebra")
  Member "lower stretch spanning trees":
    also connected through other hyperedges:
      - Hyperedge HE-7 (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)