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: "matching the universal barrier without paying the costs solving linear programs with o sqrt rank linear system solves"
  Abstract: In this paper we present a new algorithm for solving linear programs that requires only $\tilde{O}(\sqrt{rank(A)}L)$ iterations where $A$ is the constraint matrix of a linear program with $m$ constraints and $n$ variables and $L$ is the bit complexity of a linear program. Each iteration of our method consists of solving $\tilde{O}(1)$ linear systems and additional nearly linear time computation. #R##N#Our method improves upon the previous best iteration bound by factor of $\tilde{\Omega}((m/rank(A))^{1/4})$ for methods with polynomial time computable iterations and by $\tilde{\Omega}((m/rank(A))^{1/2})$ for methods which solve at most $\tilde{O}(1)$ linear systems in each iteration. Our method is parallelizable and amenable to linear algebraic techniques for accelerating the linear system solver. As such, up to polylogarithmic factors we either match or improve upon the best previous running times for solving linear programs in both depth and work for different ratios of $m$ and $rank(A)$. #R##N#Moreover, our method matches up to polylogarithmic factors a theoretical limit established by Nesterov and Nemirovski in 1994 regarding the use of a "universal barrier" for interior point methods, thereby resolving a long-standing open question regarding the running time of polynomial time interior point methods for linear programming.

Connected through hyperedge HE-1 (induced by source paper "speeding up linear programming using randomized linear algebra"):
  with members:
    - "solving tall dense linear programs in nearly linear time"
    - "solving linear programs with sqrt rank linear system solves"
    - "solving linear programs in the current matrix multiplication time"
    - "optimal approximate matrix product in terms of stable rank"
    - "efficient inverse maintenance and faster algorithms for linear programming"
    - "sketching as a tool for numerical linear algebra"
    - "path finding ii an o m sqrt n algorithm for the minimum cost flow problem"
    - "low rank approximation and regression in input sparsity time"

Connected through hyperedge HE-2 (induced by source paper "structural properties of uncoded placement optimization for coded delivery"):
  with members:
    - "the exact rate memory tradeoff for caching with uncoded prefetching"
    - "improved converses and gap results for coded caching"
    - "uncoded placement optimization for coded delivery"
    - "a new converse bound for coded caching"
    - "on the optimality of uncoded cache placement"
    - "order optimal rate of caching and coded multicasting with random demands"
    - "finite length analysis of caching aided coded multicasting"
    - "coded caching with nonuniform demands"

Connected through hyperedge HE-3 (induced by source paper "kernel based methods for bandit convex optimization"):
  with members:
    - "an optimal algorithm for bandit convex optimization"
    - "escaping the local minima via simulated annealing optimization of approximately convex functions"
    - "towards minimax policies for online linear optimization with bandit feedback"
    - "on the complexity of bandit and derivative free stochastic convex optimization"
    - "regret analysis of stochastic and nonstochastic multi armed bandit problems"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-4 (induced by source paper "ell_p row sampling by lewis weights"):
  with members:
    - "uniform sampling for matrix approximation"
    - "low rank approximation and regression in input sparsity time"
    - "iterative row sampling"
    - "osnap faster numerical linear algebra algorithms via sparser subspace embeddings"
    - "low distortion subspace embeddings in input sparsity time and applications to robust linear regression"
    - "the fast cauchy transform and faster robust linear regression"
    - "fast approximation of matrix coherence and statistical leverage"
    - "runtime guarantees for regression problems"

Connected through hyperedge HE-5 (induced by source paper "toward a unified theory of sparse dimensionality reduction in euclidean space"):
  with members:
    - "randomized sketches of convex programs with sharp guarantees"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "the johnson lindenstrauss lemma is optimal for linear dimensionality reduction"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "uniform recovery of fusion frame structured sparse signals"
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "subspace embeddings and ell_p regression using exponential random variables"
    - "on model based rip 1 matrices"

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)