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: "derandomization beyond connectivity undirected laplacian systems in nearly logarithmic space"
  Abstract: We give a deterministic $\tilde{O}(\log n)$-space algorithm for approximately solving linear systems given by Laplacians of undirected graphs, and consequently also approximating hitting times, commute times, and escape probabilities for undirected graphs. Previously, such systems were known to be solvable by randomized algorithms using $O(\log n)$ space (Doron, Le Gall, and Ta-Shma, 2017) and hence by deterministic algorithms using $O(\log^{3/2} n)$ space (Saks and Zhou, FOCS 1995 and JCSS 1999). #R##N#Our algorithm combines ideas from time-efficient Laplacian solvers (Spielman and Teng, STOC `04; Peng and Spielman, STOC `14) with ideas used to show that Undirected S-T Connectivity is in deterministic logspace (Reingold, STOC `05 and JACM `08; Rozenman and Vadhan, RANDOM `05).

Connected through hyperedge HE-1 (induced by source paper "spectral sparsification via bounded independence sampling"):
  with members:
    - "faster energy maximization for faster maximum flow"
    - "deterministic approximation of random walks in small space"
    - "high precision estimation of random walks in small space"
    - "solving directed laplacian systems in nearly linear time through sparse lu factorizations"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "faster algorithms for computing the stationary distribution simulating random walks and more"
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"
    - "efficient accelerated coordinate descent methods and faster algorithms for solving linear systems"

Connected through hyperedge HE-2 (induced by source paper "deterministic graph cuts in subquadratic time sparse balanced and k vertex"):
  with members:
    - "a faster local algorithm for detecting bounded size cuts with applications to higher connectivity problems"
    - "computing and testing small vertex connectivity in near linear time and queries"
    - "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond"
    - "distributed edge connectivity in sublinear time"
    - "breaking quadratic time for small vertex connectivity and an approximation scheme"
    - "graph sketching against adaptive adversaries applied to the minimum degree algorithm"
    - "expander decomposition and pruning faster stronger and simpler"
    - "faster algorithms for computing maximal 2 connected subgraphs in sparse directed graphs"

Connected through hyperedge HE-3 (induced by source paper "high precision estimation of random walks in small space"):
  with members:
    - "deterministic approximation of random walks in small space"
    - "solving directed laplacian systems in nearly linear time through sparse lu factorizations"
    - "faster algorithms for computing the stationary distribution simulating random walks and more"
    - "approximate gaussian elimination for laplacians fast sparse and simple"
    - "almost linear time algorithms for markov chains and new spectral primitives for directed graphs"
    - "sparsified cholesky and multigrid solvers for connection laplacians"
    - "spectral sparsification of random walk matrix polynomials"
    - "efficient accelerated coordinate descent methods and faster algorithms for solving linear systems"

Connected through hyperedge HE-4 (induced by source paper "deterministic approximation of random walks in small space"):
  with members:
    - "density independent algorithms for sparsifying k step random walks"
    - "a framework for analyzing resparsification algorithms"
    - "faster algorithms for computing the stationary distribution simulating random walks and more"
    - "almost linear time algorithms for markov chains and new spectral primitives for directed graphs"
    - "sparsified cholesky solvers for sdd linear systems"
    - "spectral sparsification of random walk matrix polynomials"
    - "scalable parallel factorizations of sdd matrices and efficient sampling for gaussian graphical models"
    - "an efficient parallel solver for sdd linear systems"

Connected through hyperedge HE-5 (induced by source paper "a deterministic linear program solver in current matrix multiplication time"):
  with members:
    - "solving empirical risk minimization in the current matrix multiplication time"
    - "dynamic matrix inverse improved algorithms and matching conditional lower bounds"
    - "distributed edge connectivity in sublinear time"
    - "limits on all known and some unknown approaches to matrix multiplication"
    - "solving linear programs in the current matrix multiplication time"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "powers of tensors and fast matrix multiplication"
    - "path finding i solving linear programs with o sqrt rank linear system solves"

Connected through hyperedge HE-6 (induced by source paper "approximating the determinant of well conditioned matrices by shallow circuits"):
  with members:
    - "the ising partition function zeros and deterministic approximation"
    - "approximating the permanent of a random matrix with vanishing mean"
    - "powers of tensors and fast matrix multiplication"

Connected through hyperedge HE-7 (induced by source paper "graph powering and spectral robustness"):
  with members:
    - "the geometric block model"
    - "robust estimators in high dimensions without the computational intractability"
    - "stochastic block model and community detection in the sparse graphs a spectral algorithm with optimal rate of recovery"
    - "semidefinite programs on sparse random graphs and their application to community detection"
    - "how robust are reconstruction thresholds for community detection"
    - "limitations in the spectral method for graph partitioning detectability threshold and localization of eigenvectors"
    - "spectral redemption in clustering sparse networks"
    - "a tutorial on spectral clustering"

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.CC(Computational Complexity)