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: "estimating current flow closeness centrality with a multigrid laplacian solver"
  Abstract: Matrices associated with graphs, such as the Laplacian, lead to numerous interesting graph problems expressed as linear systems. One field where Laplacian linear systems play a role is network analysis, e. g. for certain centrality measures that indicate if a node (or an edge) is important in the network. One such centrality measure is current-flow closeness. To allow network analysis workflows to profit from a fast Laplacian solver, we provide an implementation of the LAMG multigrid solver in the NetworKit package, facilitating the computation of current-flow closeness values or related quantities. Our main contribution consists of two algorithms that accelerate the current-flow computation for one node or a reasonably small node subset significantly. One algorithm is an unbiased estimator using sampling, the other one is based on the Johnson-Lindenstrauss transform. Our inexact algorithms lead to very accurate results in practice. Thanks to them one is now able to compute an estimation of current-flow closeness of one node on networks with tens of millions of nodes and edges within seconds or a few minutes. From a network analytical point of view, our experiments indicate that current-flow closeness can discriminate among different nodes significantly better than traditional shortest-path closeness and is also considerably more resistant to noise - we thus show that two known drawbacks of shortest-path closeness are alleviated by the current- flow variant.

Connected through hyperedge HE-1 (induced by source paper "current flow group closeness centrality for complex networks"):
  with members:
    - "kirchhoff index as a measure of edge centrality in weighted networks nearly linear time algorithms"
    - "scaling up group closeness maximization"
    - "scalable betweenness centrality maximization via sampling"
    - "measuring and maximizing influence via random walk in social activity networks"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "absorbing random walk centrality theory and algorithms"
    - "axioms for centrality"
    - "approximate maximum flow on separable undirected graphs"

Connected through hyperedge HE-2 (induced by source paper "a distributed laplacian solver and its applications to electrical flow and random spanning tree computation"):
  with members:
    - "current flow group closeness centrality for complex networks"
    - "pooling or sampling collective dynamics for electrical flow estimation"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "decentralized random walk based data collection in networks"
    - "sampling random spanning trees faster than matrix multiplication"
    - "computing maximum flow with augmenting electrical flows"
    - "a fast distributed solver for symmetric diagonally dominant linear equations"
    - "fast generation of random spanning trees and the effective resistance metric"

Connected through hyperedge HE-3 (induced by source paper "drawing large graphs by multilevel maxent stress optimization"):
  with members:
    - "a distributed multilevel force directed algorithm"
    - "a sparse stress model"
    - "is nearly linear the same in theory and practice a case study with a combinatorial laplacian solver"
    - "partitioning complex networks via size constrained clustering"
    - "coast a convex optimization approach to stress based embedding"
    - "engineering a scalable high quality graph partitioner"

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)