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 "is nearly linear the same in theory and practice a case study with a combinatorial laplacian solver"):
  Abstract: Linear system solving is one of the main workhorses in applied mathematics. Recently, theoretical computer scientists have contributed sophisticated algorithms for solving linear systems with symmetric diagonally dominant matrices (a class to which Laplacian matrices belong) in provably nearly-linear time. While these algorithms are highly interesting from a theoretical perspective, there are no published results how they perform in practice. #R##N#With this paper we address this gap. We provide the first implementation of the combinatorial solver by [Kelner et al., STOC 2013], which is particularly appealing for implementation due to its conceptual simplicity. The algorithm exploits that a Laplacian matrix corresponds to a graph; solving Laplacian linear systems amounts to finding an electrical flow in this graph with the help of cycles induced by a spanning tree with the low-stretch property. #R##N#The results of our comprehensive experimental study are ambivalent. They confirm a nearly-linear running time, but for reasonable inputs the constant factors make the solver much slower than methods with higher asymptotic complexity. One other aspect predicted by theory is confirmed by our findings, though: Spanning trees with lower stretch indeed reduce the solver's running time. Yet, simple spanning tree algorithms perform in practice better than those with a guaranteed low stretch.

Includes members:
  Member "simple parallel and distributed algorithms for spectral graph sparsification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "slim graph practical lossy graph compression for approximate graph processing storage and analytics")
  Member "networkit an interactive tool suite for high performance network analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient community detection in boolean composed multiplex networks")
  Member "networkit a tool suite for large scale complex network analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "combined centrality measures for an improved characterization of influence spread in social networks")
  Member "an efficient parallel solver for sdd linear systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "circulation control for faster minimum cost flow in unit capacity graphs")
  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 "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a spectral approach to 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)