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: "kirchhoff index as a measure of edge centrality in weighted networks nearly linear time algorithms"
  Abstract: Estimating the relative importance of vertices and edges is a fundamental issue in the analysis of complex networks, and has found vast applications in various aspects, such as social networks, power grids, and biological networks. Most previous work focuses on metrics of vertex importance and methods for identifying powerful vertices, while related work for edges is much lesser, especially for weighted networks, due to the computational challenge. In this paper, we propose to use the well-known Kirchhoff index as the measure of edge centrality in weighted networks, called θ-Kirchhoff edge centrality. The Kirchhoff index of a network is defined as the sum of effective resistances over all vertex pairs. The centrality of an edge e is reflected in the increase of Kirchhoff index of the network when the edge e is partially deactivated, characterized by a parameter θ. We define two equivalent measures for θ-Kirchhoff edge centrality. Both are global metrics and have a better discriminating power than commonly used measures, based on local or partial structural information of networks, e.g. edge betweenness and spanning edge centrality.   Despite the strong advantages of Kirchhoff index as a centrality measure and its wide applications, computing the exact value of Kirchhoff edge centrality for each edge in a graph is computationally demanding. To solve this problem, for each of the θ-Kirchhoff edge centrality metrics, we present an efficient algorithm to compute its ϵ-approximation for all the m edges in nearly linear time in m. The proposed θ-Kirchhoff edge centrality is the first global metric of edge importance that can be provably approximated in nearly-linear time. Moreover, according to the θ-Kirchhoff edge centrality, we present a θ-Kirchhoff vertex centrality measure, as well as a fast algorithm that can compute ϵ-approximate Kirchhoff vertex centrality for all the n vertices in nearly linear time in m.

Connected through hyperedge HE-1 (induced by source paper "fully dynamic spectral vertex sparsifiers and applications"):
  with members:
    - "dynamic low stretch trees via dynamic low diameter decompositions"
    - "graph reduction by local variation"
    - "dynamic effective resistances and approximate schur complement on separable graphs"
    - "on solving linear systems in sublinear time"
    - "spectrally approximating large graphs with smaller graphs"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "density independent algorithms for sparsifying k step random walks"
    - "the power of vertex sparsifiers in dynamic graph algorithms"

Connected through hyperedge HE-2 (induced by source paper "current flow group closeness centrality for complex networks"):
  with members:
    - "scaling up group closeness maximization"
    - "estimating current flow closeness centrality with a multigrid laplacian solver"
    - "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-3 (induced by source paper "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"):
  with members:
    - "current flow group closeness centrality for complex networks"
    - "massively parallel algorithms for finding well connected components in sparse graphs"
    - "fully dynamic effective resistances"
    - "spectrum approximation beyond fast matrix multiplication algorithms and hardness"
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "nearly tight bounds for sandpile transience on the grid"
    - "optimal lower bounds for sketching graph cuts"

Connected through hyperedge HE-4 (induced by source paper "maximizing the number of spanning trees in a connected graph"):
  with members:
    - "current flow group closeness centrality for complex networks"
    - "fully dynamic spectral vertex sparsifiers and applications"
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "a nearly linear time algorithm for submodular maximization with a knapsack constraint"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "subdeterminant maximization via nonconvex relaxations and anti concentration"
    - "determinant preserving sparsification of sddm matrices with applications to counting and sampling spanning trees"
    - "sampling random spanning trees faster than matrix multiplication"

Connected through hyperedge HE-5 (induced by source paper "fully dynamic effective resistances"):
  with members:
    - "density independent algorithms for sparsifying k step random walks"
    - "the power of vertex sparsifiers in dynamic graph algorithms"
    - "dynamic minimum spanning forest with subpolynomial worst case update time"
    - "fully dynamic maximal matching in constant update time"
    - "fully dynamic all pairs shortest paths with worst case update time revisited"
    - "incremental exact min cut in poly logarithmic amortized update time"
    - "faster fully dynamic minimum spanning forest"
    - "dynamic approximate all pairs shortest paths breaking the o mn barrier and derandomization"

Connected through hyperedge HE-6 (induced by source paper "optimal offline dynamic 2 3 edge vertex connectivity"):
  with members:
    - "fully dynamic spectral vertex sparsifiers and applications"
    - "maximizing the number of spanning trees in a connected graph"
    - "dynamic effective resistances and approximate schur complement on separable graphs"
    - "dynamic bridge finding in tilde o log 2 n amortized time"
    - "the power of vertex sparsifiers in dynamic graph algorithms"
    - "fully dynamic connectivity in o log n log log n 2 amortized expected time"
    - "sampling random spanning trees faster than matrix multiplication"
    - "on the hardness of partially dynamic graph problems and connections to diameter"

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)