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: "efficient accelerated coordinate descent methods and faster algorithms for solving linear systems"
  Abstract: In this paper we show how to accelerate randomized coordinate descent methods and achieve faster convergence rates without paying per-iteration costs in asymptotic running time. In particular, we show how to generalize and efficiently implement a method proposed by Nesterov, giving faster asymptotic running times for various algorithms that use standard coordinate descent as a black box. In addition to providing a proof of convergence for this new general method, we show that it is numerically stable, efficiently implementable, and in certain regimes, asymptotically optimal. #R##N#To highlight the computational power of this algorithm, we show how it can used to create faster linear system solvers in several regimes: #R##N#- We show how this method achieves a faster asymptotic runtime than conjugate gradient for solving a broad class of symmetric positive definite systems of equations. #R##N#- We improve the best known asymptotic convergence guarantees for Kaczmarz methods, a popular technique for image reconstruction and solving overdetermined systems of equations, by accelerating a randomized algorithm of Strohmer and Vershynin. #R##N#- We achieve the best known running time for solving Symmetric Diagonally Dominant (SDD) system of equations in the unit-cost RAM model, obtaining an O(m log^{3/2} n (log log n)^{1/2} log (log n / eps)) asymptotic running time by accelerating a recent solver by Kelner et al. #R##N#Beyond the independent interest of these solvers, we believe they highlight the versatility of the approach of this paper and we hope that they will open the door for further algorithmic improvements in the future.

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 "dynamic streaming spectral sparsification in nearly linear time and space"):
  with members:
    - "faster spectral sparsification in dynamic streams"
    - "optimal lower bounds for distributed and streaming spanning forest computation"
    - "spectral subspace sparsification"
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "optimal lower bounds for universal relation and for samplers and finding duplicates in streams"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "density independent algorithms for sparsifying k step random walks"

Connected through hyperedge HE-3 (induced by source paper "flows in almost linear time via adaptive preconditioning"):
  with members:
    - "graph sparsification spectral sketches and faster resistance computation via short cycle decompositions"
    - "expander decomposition and pruning faster stronger and simpler"
    - "solving directed laplacian systems in nearly linear time through sparse lu factorizations"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "much faster algorithms for matrix scaling"
    - "matrix scaling and balancing via box constrained newton s method and interior point methods"
    - "near optimal approximate shortest paths and transshipment in distributed and streaming models"
    - "asymptotic behavior of ell_p based laplacian regularization in semi supervised learning"

Connected through hyperedge HE-4 (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-5 (induced by source paper "high precision estimation of random walks in small space"):
  with members:
    - "deterministic approximation of random walks in small space"
    - "solving directed laplacian systems in nearly linear time through sparse lu factorizations"
    - "derandomization beyond connectivity undirected laplacian systems in nearly logarithmic space"
    - "faster algorithms for computing the stationary distribution simulating random walks and more"
    - "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"
    - "spectral sparsification of random walk matrix polynomials"

Connected through hyperedge HE-6 (induced by source paper "computing circle packing representations of planar graphs"):
  with members:
    - "matrix scaling and balancing via box constrained newton s method and interior point methods"
    - "strongly monotone drawings of planar graphs"
    - "approximate gaussian elimination for laplacians fast sparse and simple"
    - "sparsified cholesky and multigrid solvers for connection laplacians"
    - "the galois complexity of graph drawing why numerical solutions are ubiquitous for force directed spectral and circle packing drawings"
    - "balanced circle packings for planar graphs"
    - "an efficient parallel solver for sdd linear systems"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"

Connected through hyperedge HE-7 (induced by source paper "memory sample tradeoffs for linear regression with small error"):
  with members:
    - "parallelization does not accelerate convex optimization adaptivity lower bounds for non smooth convex minimization"
    - "detecting correlations with little memory and communication"
    - "extractor based time space lower bounds for learning"
    - "fast learning requires good memory a time space lower bound for parity learning"
    - "communication lower bounds for statistical estimation problems via a distributed data processing inequality"
    - "fundamental limits of online and distributed algorithms for statistical learning and estimation"
    - "on the complexity of bandit and derivative free stochastic convex optimization"
    - "a concentration theorem for projections"

Connected through hyperedge HE-8 (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"

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)