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 "sparse sums of positive semidefinite matrices"):
  Abstract: Many fast graph algorithms begin by preprocessing the graph to improve its sparsity. A common form of this is spectral sparsification, which involves removing and reweighting the edges of the graph while approximately preserving its spectral properties. This task has a more general linear algebraic formulation in terms of approximating sums of rank-one matrices. This article considers a more general task of approximating sums of symmetric, positive semidefinite matrices of arbitrary rank. We present two deterministic, polynomial time algorithms for solving this problem. The first algorithm applies the pessimistic estimators of Wigderson and Xiao, and the second involves an extension of the method of Batson, Spielman, and Srivastava. These algorithms have several applications, including sparsifiers of hypergraphs, sparse solutions to semidefinite programs, sparsifiers of unique games, and graph sparsifiers with various auxiliary constraints.

Includes members:
  Member "a matrix hyperbolic cosine algorithm and applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "spectral sparsification via bounded independence sampling")
  Member "faster spectral sparsification and numerical algorithms for sdd matrices":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fully dynamic spectral vertex sparsifiers and applications")
  Member "a parallel approximation algorithm for positive semidefinite programming":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "positive semidefinite programming mixed parallel and width independent")
  Member "near optimal column based matrix reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "good feature matching towards accurate robust vo vslam with low latency")
  Member "graph sparsification by edge connectivity and random spanning trees":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "random spanning trees for expanders sparsifiers and virtual network security")
  Member "towards an sdp based approach to spectral methods a nearly linear time algorithm for graph partitioning and decomposition":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond")
  Member "a linear time algorithm for sparsification of unweighted graphs":
    also connected through other hyperedges:
      (none)
  Member "graph sparsification via refinement sampling":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "sublinear algorithms for maxcut and correlation clustering")

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.DM(Discrete Mathematics)