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 ii an o m sqrt n algorithm for the minimum cost flow problem"
  Abstract: In this paper we present an $\tilde{O}(m\sqrt{n}\log^{O(1)}U)$ time algorithm for solving the maximum flow problem on directed graphs with $m$ edges, $n$ vertices, and capacity ratio $U$. This improves upon the previous fastest running time of $O(m\min\left(n^{2/3},m^{1/2}\right)\log\left(n^{2}/m\right)\log U)$ achieved over 15 years ago by Goldberg and Rao. In the special case of solving dense directed unit capacity graphs our algorithm improves upon the previous fastest running times of of $O(\min\{m^{3/2},mn^{^{2/3}}\})$ achieved by Even and Tarjan and Karzanov over 35 years ago and of $\tilde{O}(m^{10/7})$ achieved recently by M\k{a}dry. #R##N#We achieve these results through the development and application of a new general interior point method that we believe is of independent interest. The number of iterations required by this algorithm is better than that predicted by analyzing the best self-concordant barrier of the feasible region. By applying this method to the linear programming formulations of maximum flow, minimum cost flow, and lossy generalized minimum cost flow and applying analysis by Daitch and Spielman we achieve running time of $\tilde{O}(m\sqrt{n}\log^{O(1)}(U/\epsilon))$ for these problems as well. Furthermore, our algorithm is parallelizable and using a recent nearly linear time work polylogarithmic depth Laplacian system solver of Spielman and Peng we achieve a $\tilde{O}(\sqrt{n}\log^{O(1)}(U/\epsilon))$ depth algorithm and $\tilde{O}(m\sqrt{n}\log^{O(1)}(U/\epsilon))$ work algorithm for solving these problems.

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"
    - "matching the universal barrier without paying the costs solving linear programs with o sqrt rank linear system solves"
    - "low rank approximation and regression in input sparsity time"

Connected through hyperedge HE-2 (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-3 (induced by source paper "conditional hardness of earth mover distance"):
  with members:
    - "preconditioning for the geometric transportation problem"
    - "faster algorithms for the geometric transportation problem"
    - "hardness of approximate nearest neighbor search"
    - "on the hardness of approximate and exact bichromatic maximum inner product"
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "polynomial representations of threshold functions and algorithmic applications"
    - "quadratic conditional lower bounds for string problems and dynamic time warping"
    - "edit distance cannot be computed in strongly subquadratic time unless seth is false"

Connected through hyperedge HE-4 (induced by source paper "a weighted approach to the maximum cardinality bipartite matching problem with applications in geometric settings"):
  with members:
    - "faster algorithms for the geometric transportation problem"
    - "a faster algorithm for minimum cost bipartite matching in minor free graphs"
    - "the weighted matching approach to maximum cardinality matching"
    - "navigating central path with electrical flows from flows to matchings and back"
    - "multiple source multiple sink maximum flow in directed planar graphs in near linear time"

Connected through hyperedge HE-5 (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 i solving linear programs with o sqrt rank linear system solves"
    - "low rank approximation and regression in input sparsity time"
    - "iterative row sampling"

Connected through hyperedge HE-6 (induced by source paper "a faster cutting plane method and its implications for combinatorial and convex optimization"):
  with members:
    - "using optimization to obtain a width independent parallel simpler and faster positive sdp solver"
    - "efficient inverse maintenance and faster algorithms for linear programming"
    - "powers of tensors and fast matrix multiplication"
    - "bayesian truthful mechanisms for job scheduling from bi criterion approximation algorithms"
    - "uniform sampling for matrix approximation"
    - "reducing revenue to welfare maximization approximation algorithms and other generalizations"
    - "optimal multi dimensional mechanism design reducing revenue to welfare maximization"
    - "iterative row sampling"

Connected through hyperedge HE-7 (induced by source paper "sparsified cholesky solvers for sdd linear systems"):
  with members:
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"
    - "simple parallel and distributed algorithms for spectral graph sparsification"
    - "uniform sampling for matrix approximation"
    - "towards an sdp based approach to spectral methods a nearly linear time algorithm for graph partitioning and decomposition"
    - "nearly linear time algorithms for preconditioning and solving symmetric diagonally dominant linear systems"
    - "solving elliptic finite element systems in near linear time with support preconditioners"

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)