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: "a matrix hyperbolic cosine algorithm and applications"
  Abstract: In this paper, we generalize Spencer's hyperbolic cosine algorithm to the matrix-valued setting. We apply the proposed algorithm to several problems by analyzing its computational efficiency under two special cases of matrices; one in which the matrices have a group structure and an other in which they have rank-one. As an application of the former case, we present a deterministic algorithm that, given the multiplication table of a finite group of size n, it constructs an expanding Cayley graph of logarithmic degree in near-optimal $\mathcal{O}(n^2\log^3 n)$ time. For the latter case, we present a fast deterministic algorithm for spectral sparsification of positive semi-definite matrices, which implies an improved deterministic algorithm for spectral graph sparsification of dense graphs. In addition, we give an elementary connection between spectral sparsification of positive semi-definite matrices and element-wise matrix sparsification. As a consequence, we obtain improved element-wise sparsification algorithms for diagonally dominant-like matrices.

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"
    - "derandomization beyond connectivity undirected laplacian systems in nearly logarithmic space"
    - "faster algorithms for computing the stationary distribution simulating random walks and more"
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"

Connected through hyperedge HE-2 (induced by source paper "linear size sparsifier and the geometry of the operator norm ball"):
  with members:
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "the gram schmidt walk a cure for the banaszczyk blues"
    - "constructive discrepancy minimization for convex sets"
    - "constructive algorithms for discrepancy minimization"
    - "twice ramanujan sparsifiers"

Connected through hyperedge HE-3 (induced by source paper "an sdp based algorithm for linear sized spectral sparsification"):
  with members:
    - "using optimization to obtain a width independent parallel simpler and faster positive sdp solver"
    - "sparsified cholesky and multigrid solvers for connection laplacians"
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"
    - "constructing linear sized spectral sparsification in almost linear time"
    - "uniform sampling for matrix approximation"
    - "an efficient parallel solver for sdd linear systems"
    - "iterative row sampling"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"

Connected through hyperedge HE-4 (induced by source paper "on fully dynamic graph sparsifiers"):
  with members:
    - "sparse sums of positive semidefinite matrices"
    - "unifying and strengthening hardness for dynamic problems via the online matrix vector multiplication conjecture"
    - "sparsified cholesky and multigrid solvers for connection laplacians"
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"
    - "constructing linear sized spectral sparsification in almost linear time"
    - "algorithms for lipschitz learning on graphs"
    - "sketching cuts in graphs and hypergraphs"
    - "approximate undirected maximum flows in o m polylog n time"

Connected through hyperedge HE-5 (induced by source paper "sparse sums of positive semidefinite matrices"):
  with members:
    - "faster spectral sparsification and numerical algorithms for sdd matrices"
    - "a parallel approximation algorithm for positive semidefinite programming"
    - "near optimal column based matrix reconstruction"
    - "graph sparsification by edge connectivity and random spanning trees"
    - "towards an sdp based approach to spectral methods a nearly linear time algorithm for graph partitioning and decomposition"
    - "a linear time algorithm for sparsification of unweighted graphs"
    - "graph sparsification via refinement sampling"
    - "approaching optimality for solving sdd systems"

Connected through hyperedge HE-6 (induced by source paper "deterministic discrepancy minimization via the multiplicative weight update method"):
  with members:
    - "constructive discrepancy minimization by walking on the edges"
    - "constructive algorithms for discrepancy minimization"

Connected through hyperedge HE-7 (induced by source paper "spectral sparsification and regret minimization beyond matrix multiplicative updates"):
  with members:
    - "sparse sums of positive semidefinite matrices"
    - "using optimization to break the epsilon barrier a faster and simpler width independent algorithm for solving positive linear programs in parallel"
    - "an efficient algorithm for unweighted spectral graph sparsification"
    - "an almost linear time algorithm for approximate max flow in undirected graphs and its multicommodity generalizations"
    - "an efficient parallel solver for sdd linear systems"
    - "near optimal algorithms for online matrix prediction"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "min max graph partitioning and small set expansion"

Connected through hyperedge HE-8 (induced by source paper "constructing linear sized spectral sparsification in almost linear time"):
  with members:
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"
    - "sparsified cholesky solvers for sdd linear systems"
    - "uniform sampling for matrix approximation"
    - "single pass spectral sparsification in dynamic streams"
    - "an almost linear time algorithm for approximate max flow in undirected graphs and its multicommodity generalizations"
    - "an efficient parallel solver for sdd linear systems"
    - "nearly maximum flows in nearly linear time"
    - "iterative row sampling"

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)