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: "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
  Abstract: In this paper, we present a simple combinatorial algorithm that solves symmetric diagonally dominant (SDD) linear systems in nearly-linear time. It uses very little of the machinery that previously appeared to be necessary for a such an algorithm. It does not require recursive preconditioning, spectral sparsification, or even the Chebyshev Method or Conjugate Gradient. After constructing a "nice" spanning tree of a graph associated with the linear system, the entire algorithm consists of the repeated application of a simple (non-recursive) update rule, which it implements using a lightweight data structure. The algorithm is numerically stable and can be implemented without the increased bit-precision required by previous solvers. As such, the algorithm has the fastest known running time under the standard unit-cost RAM model. We hope that the simplicity of the algorithm and the insights yielded by its analysis will be useful in both theory and practice.

Connected through hyperedge HE-1 (induced by source paper "circulation control for faster minimum cost flow in unit capacity graphs"):
  with members:
    - "faster p norm minimizing flows via smoothed q norm problems"
    - "faster energy maximization for faster maximum flow"
    - "flows in almost linear time via adaptive preconditioning"
    - "computing maximum flow with augmenting electrical flows"
    - "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"
    - "an efficient parallel solver for sdd linear systems"
    - "navigating central path with electrical flows from flows to matchings and back"

Connected through hyperedge HE-2 (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-3 (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-4 (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-5 (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-6 (induced by source paper "a near optimal algorithm for approximating the john ellipsoid"):
  with members:
    - "solving empirical risk minimization in the current matrix multiplication time"
    - "total least squares regression in input sparsity time"
    - "nearly optimal dynamic k means clustering for high dimensional data"
    - "sensitivity sampling over dynamic geometric data streams with applications to k clustering"
    - "subspace embedding and linear regression with orlicz norm"
    - "relative error tensor low rank approximation"
    - "near optimal sketching of low rank tensor regression"
    - "sketching for kronecker product regression and p splines"

Connected through hyperedge HE-7 (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-8 (induced by source paper "dynamic low stretch trees via dynamic low diameter decompositions"):
  with members:
    - "faster distributed shortest path approximations via shortcuts"
    - "expander decomposition and pruning faster stronger and simpler"
    - "a deamortization approach for dynamic spanner and dynamic maximal matching"
    - "dynamic minimum spanning forest with subpolynomial worst case update time"
    - "fully dynamic connectivity in o log n log log n 2 amortized expected time"
    - "efficient algorithms for constructing very sparse spanners and emulators"
    - "fully dynamic minimum spanning forest with improved worst case update time"
    - "on fully dynamic graph sparsifiers"

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)