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: "path finding i 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 to solve a linear program with $m$ constraints, $n$ variables, and constraint matrix $A$, and bit complexity $L$. 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 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 "solving linear programs with sqrt rank linear system solves"):
  with members:
    - "solving linear programs in the current matrix multiplication time"
    - "approximate gaussian elimination for laplacians fast sparse and simple"
    - "sparsified cholesky and multigrid solvers for connection laplacians"
    - "sparsified cholesky solvers for sdd linear systems"
    - "efficient inverse maintenance and faster algorithms for linear programming"
    - "sketching as a tool for numerical linear algebra"
    - "uniform sampling for matrix approximation"
    - "an efficient parallel solver for sdd linear systems"

Connected through hyperedge HE-2 (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"
    - "derandomization beyond connectivity undirected laplacian systems in nearly logarithmic space"
    - "improved rectangular matrix multiplication using powers of the coppersmith winograd tensor"
    - "powers of tensors and fast matrix multiplication"

Connected through hyperedge HE-3 (induced by source paper "efficient inverse maintenance and faster algorithms for linear programming"):
  with members:
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "powers of tensors and fast matrix multiplication"
    - "ell_p row sampling by lewis weights"
    - "uniform sampling for matrix approximation"
    - "an almost linear time algorithm for approximate max flow in undirected graphs and its multicommodity generalizations"
    - "path finding ii an o m sqrt n algorithm for the minimum cost flow problem"
    - "low rank approximation and regression in input sparsity 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)