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: "faster approximate multicommodity flow using quadratically coupled flows"
  Abstract: The maximum multicommodity flow problem is a natural generalization of the maximum flow problem to route multiple distinct flows. Obtaining a $1-\epsilon$ approximation to the multicommodity flow problem on graphs is a well-studied problem. In this paper we present an adaptation of recent advances in single-commodity flow algorithms to this problem. As the underlying linear systems in the electrical problems of multicommodity flow problems are no longer Laplacians, our approach is tailored to generate specialized systems which can be preconditioned and solved efficiently using Laplacians. Given an undirected graph with m edges and k commodities, we give algorithms that find $1-\epsilon$ approximate solutions to the maximum concurrent flow problem and the maximum weighted multicommodity flow problem in time $\tilde{O}(m^{4/3}\poly(k,\epsilon^{-1}))$.

Connected through hyperedge HE-1 (induced by source paper "faster divergence maximization for faster maximum flow"):
  with members:
    - "circulation control for faster minimum cost flow in unit capacity graphs"
    - "solving tall dense linear programs in nearly linear time"
    - "a deterministic algorithm for balanced cut with applications to dynamic connectivity flows and beyond"
    - "solving linear programs with sqrt rank linear system solves"
    - "faster p norm minimizing flows via smoothed q norm problems"
    - "fast provably convergent irls algorithm for p norm linear regression"
    - "faster energy maximization for faster maximum flow"
    - "flows in almost linear time via adaptive preconditioning"

Connected through hyperedge HE-2 (induced by source paper "faster energy maximization for faster maximum flow"):
  with members:
    - "solving linear programs with sqrt rank linear system solves"
    - "min cost flow in unit capacity planar graphs"
    - "flows in almost linear time via adaptive preconditioning"
    - "solving linear programs in the current matrix multiplication time"
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "routing under balance"
    - "computing maximum flow with augmenting electrical flows"

Connected through hyperedge HE-3 (induced by source paper "iterative refinement for ell_p norm regression"):
  with members:
    - "solving linear programs in the current matrix multiplication time"
    - "much faster algorithms for matrix scaling"
    - "matrix scaling and balancing via box constrained newton s method and interior point methods"
    - "computing maximum flow with augmenting electrical flows"
    - "on fully dynamic graph sparsifiers"
    - "approximate gaussian elimination for laplacians fast sparse and simple"
    - "sparsified cholesky and multigrid solvers for connection laplacians"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"

Connected through hyperedge HE-4 (induced by source paper "current flow group closeness centrality for complex networks"):
  with members:
    - "kirchhoff index as a measure of edge centrality in weighted networks nearly linear time algorithms"
    - "scaling up group closeness maximization"
    - "estimating current flow closeness centrality with a multigrid laplacian solver"
    - "scalable betweenness centrality maximization via sampling"
    - "measuring and maximizing influence via random walk in social activity networks"
    - "a faster cutting plane method and its implications for combinatorial and convex optimization"
    - "absorbing random walk centrality theory and algorithms"
    - "axioms for centrality"

Connected through hyperedge HE-5 (induced by source paper "solving directed laplacian systems in nearly linear time through sparse lu factorizations"):
  with members:
    - "sampling random spanning trees faster than matrix multiplication"
    - "faster algorithms for computing the stationary distribution simulating random walks and more"
    - "computing maximum flow with augmenting electrical flows"
    - "on fully dynamic graph sparsifiers"
    - "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"
    - "sparsified cholesky solvers for sdd linear systems"

Connected through hyperedge HE-6 (induced by source paper "hardness results for structured linear systems"):
  with members:
    - "computing maximum flow with augmenting electrical flows"
    - "geometric median in nearly linear time"
    - "sparsified cholesky and multigrid solvers for connection laplacians"
    - "efficient inverse maintenance and faster algorithms for linear programming"
    - "sketching as a tool for numerical linear algebra"
    - "approximate undirected maximum flows in o m polylog n time"
    - "an almost linear time algorithm for approximate max flow in undirected graphs and its multicommodity generalizations"
    - "stable camera motion estimation using convex programming"

Connected through hyperedge HE-7 (induced by source paper "faster algorithms for computing the stationary distribution simulating random walks and more"):
  with members:
    - "geodesic walks in polytopes"
    - "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"
    - "sparsified cholesky solvers for sdd linear systems"
    - "fast generation of random spanning trees and the effective resistance metric"
    - "effective resistance reducing flows spectrally thin trees and asymmetric tsp"
    - "approximate undirected maximum flows in o m polylog n time"

Connected through hyperedge HE-8 (induced by source paper "approximate gaussian elimination for laplacians fast sparse and simple"):
  with members:
    - "sparsified cholesky and multigrid solvers for connection laplacians"
    - "fast provable algorithms for isotonic regression in all ell_ p norms"
    - "uniform sampling for matrix approximation"
    - "an efficient parallel solver for sdd linear systems"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "navigating central path with electrical flows from flows to matchings and back"
    - "faster spectral sparsification and numerical algorithms for sdd matrices"
    - "a nearly mlogn time solver for sdd linear systems"

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)