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: "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.

Connected through hyperedge HE-1 (induced by source paper "an empirical study of cycle toggling based laplacian solvers"):
  with members:
    - "fast provable algorithms for isotonic regression in all ell_ p norms"
    - "efficient accelerated coordinate descent methods and faster algorithms for solving linear systems"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "runtime guarantees for regression problems"
    - "approaching optimality for solving sdd systems"
    - "nearly linear time algorithms for preconditioning and solving symmetric diagonally dominant linear systems"
    - "benchmarking optimization software with performance profiles"

Connected through hyperedge HE-2 (induced by source paper "estimating current flow closeness centrality with a multigrid laplacian solver"):
  with members:
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs"
    - "fast approximation of centrality"

Connected through hyperedge HE-3 (induced by source paper "drawing large graphs by multilevel maxent stress optimization"):
  with members:
    - "estimating current flow closeness centrality with a multigrid laplacian solver"
    - "a distributed multilevel force directed algorithm"
    - "a sparse stress model"
    - "partitioning complex networks via size constrained clustering"
    - "coast a convex optimization approach to stress based embedding"
    - "engineering a scalable high quality graph partitioner"

Connected through hyperedge HE-4 (induced by source paper "evaluating the potential of a dual randomized kaczmarz solver for laplacian linear systems"):
  with members:
    - "a scalable generative graph model with community structure"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "approaching optimality for solving sdd systems"
    - "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs"
    - "solving elliptic finite element systems in near linear time with support preconditioners"

Connected through hyperedge HE-5 (induced by source paper "evaluating the potential of a laplacian linear solver"):
  with members:
    - "a scalable generative graph model with community structure"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "approaching optimality for solving sdd systems"

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)