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: "computing the log determinant of symmetric diagonally dominant matrices in near linear time"
  Abstract: We present new algorithms for computing the log-determinant of symmetric, diagonally dominant matrices. Existing algorithms run with cubic complexity with respect to the size of the matrix in the worst case. Our algorithm computes an approximation of the log-determinant in time near-linear with respect to the number of non-zero entries and with high probability. This algorithm builds upon the utra-sparsifiers introduced by Spielman and Teng for Laplacian matrices and ultimately uses their refined versions introduced by Koutis, Miller and Peng in the context of solving linear systems. We also present simpler algorithms that compute upper and lower bounds and that may be of more immediate practical interest.

Connected through hyperedge HE-1 (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"
    - "an efficient parallel solver for sdd linear systems"

Connected through hyperedge HE-2 (induced by source paper "a randomized algorithm for approximating the log determinant of a symmetric positive definite matrix"):
  with members:
    - "approximating the spectral sums of large scale matrices using chebyshev approximations"
    - "large scale log determinant computation through stochastic chebyshev expansions"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear 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.NA(Numerical Analysis)