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: "expanders via random spanning trees"
  Abstract: Motivated by the problem of routing reliably and scalably in a graph, we introduce the notion of a splicer, the union of spanning trees of a graph. We prove that for any bounded-degree n-vertex graph, the union of two random spanning trees approximates the expansion of every cut of the graph to within a factor of O(log n). For the random graph G_{n,p}, for p> c log{n}/n, two spanning trees give an expander. This is suggested by the case of the complete graph, where we prove that two random spanning trees give an expander. The construction of the splicer is elementary -- each spanning tree can be produced independently using an algorithm by Aldous and Broder: a random walk in the graph with edges leading to previously unvisited vertices included in the tree. #R##N#A second important application of splicers is to graph sparsification where the goal is to approximate every cut (and more generally the quadratic form of the Laplacian) using only a small subgraph of the original graph. Benczur-Karger as well as Spielman-Srivastava have shown sparsifiers with O(n log n/eps^2)$ edges that achieve approximation within factors 1+eps and 1-eps. Their methods, based on independent sampling of edges, need Omega(n log n) edges to get any approximation (else the subgraph could be disconnected) and leave open the question of linear-size sparsifiers. Splicers address this question for random graphs by providing sparsifiers of size O(n) that approximate every cut to within a factor of O(log n).

Connected through hyperedge HE-1 (induced by source paper "random spanning trees for expanders sparsifiers and virtual network security"):
  with members:
    - "software defined networking a comprehensive survey"
    - "random walks which prefer unvisited edges exploring high girth even degree expanders in linear time"
    - "graph sparsification by edge connectivity and random spanning trees"
    - "twice ramanujan sparsifiers"
    - "spectral sparsification of graphs"
    - "graph sparsification by effective resistances"

Connected through hyperedge HE-2 (induced by source paper "determinant preserving sparsification of sddm matrices with applications to counting and sampling spanning trees"):
  with members:
    - "density independent algorithms for sparsifying k step random walks"
    - "sampling random spanning trees faster than matrix multiplication"
    - "almost linear time algorithms for markov chains and new spectral primitives for directed graphs"
    - "a randomized algorithm for approximating the log determinant of a symmetric positive definite matrix"
    - "sparsified cholesky and multigrid solvers for connection laplacians"
    - "large scale log determinant computation through stochastic chebyshev expansions"
    - "fast generation of random spanning trees and the effective resistance metric"
    - "computing the log determinant of symmetric diagonally dominant matrices in near linear time"

Connected through hyperedge HE-3 (induced by source paper "an almost linear time algorithm for uniform random spanning tree generation"):
  with members:
    - "determinant preserving sparsification of sddm matrices with applications to counting and sampling spanning trees"
    - "localization of electrical flows"
    - "sampling random spanning trees faster than matrix multiplication"
    - "approximate gaussian elimination for laplacians fast sparse and simple"
    - "sparsified cholesky solvers for sdd linear systems"
    - "fast generation of random spanning trees and the effective resistance metric"
    - "an efficient parallel solver for sdd linear systems"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"

Connected through hyperedge HE-4 (induced by source paper "sampling random spanning trees faster than matrix multiplication"):
  with members:
    - "approximate gaussian elimination for laplacians fast sparse and simple"
    - "sparsified cholesky and multigrid solvers for connection laplacians"
    - "fast generation of random spanning trees and the effective resistance metric"
    - "efficient accelerated coordinate descent methods and faster algorithms for solving linear systems"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "faster spectral sparsification and numerical algorithms for sdd matrices"
    - "a nearly mlogn time solver for sdd linear systems"
    - "faster generation of random spanning trees"

Connected through hyperedge HE-5 (induced by source paper "fast generation of random spanning trees and the effective resistance metric"):
  with members:
    - "an almost linear time algorithm for approximate max flow in undirected graphs and its multicommodity generalizations"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "navigating central path with electrical flows from flows to matchings and back"
    - "nearly maximum flows in nearly linear time"
    - "a nearly mlogn time solver for sdd linear systems"
    - "fast approximation algorithms for cut based problems in undirected graphs"
    - "approaching optimality for solving sdd systems"
    - "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs"

Connected through hyperedge HE-6 (induced by source paper "distributed random walks"):
  with members:
    - "near optimal random walk sampling in distributed networks"
    - "fast distributed computation in dynamic networks via random walks"
    - "distributed verification and hardness of distributed approximation"
    - "faster generation of random spanning trees"
    - "efficient distributed random walks with applications"
    - "lower bounds for distributed markov chain problems"
    - "search in power law networks"

Connected through hyperedge HE-7 (induced by source paper "near optimal random walk sampling in distributed networks"):
  with members:
    - "efficient distributed random walks with applications"
    - "search in power law networks"

Connected through hyperedge HE-8 (induced by source paper "physical expander in virtual tree overlay"):
  with members:
    - "xheal localized self healing using expanders"

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.DM(Discrete Mathematics)