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: "solving elliptic finite element systems in near linear time with support preconditioners"
  Abstract: We consider linear systems arising from the use of the finite element method for solving scalar linear elliptic problems. Our main result is that these linear systems, which are symmetric and positive semidefinite, are well approximated by symmetric diagonally dominant matrices. Our framework for defining matrix approximation is support theory. Significant graph theoretic work has already been developed in the support framework for preconditioners in the diagonally dominant case, and in particular it is known that such systems can be solved with iterative methods in nearly linear time. Thus, our approximation result implies that these graph theoretic techniques can also solve a class of finite element problems in nearly linear time. We show that the support number bounds, which control the number of iterations in the preconditioned iterative solver, depend on mesh quality measures but not on the problem size or shape of the domain.

Connected through hyperedge HE-1 (induced by source paper "efficient structured matrix recovery and nearly linear time algorithms for solving inverse symmetric m matrices"):
  with members:
    - "non convex matrix completion against a semi random adversary"
    - "an almost linear time algorithm for uniform random spanning tree generation"
    - "matrix scaling and balancing via box constrained newton s method and interior point methods"
    - "sampling random spanning trees faster than matrix multiplication"
    - "faster algorithms for computing the stationary distribution simulating random walks and more"
    - "spectral sparsification and regret minimization beyond matrix multiplicative updates"
    - "on sketching quadratic forms"
    - "algorithms for lipschitz learning on graphs"

Connected through hyperedge HE-2 (induced by source paper "spectral sparsification of simplicial complexes for clustering and label propagation"):
  with members:
    - "near optimal sketching of low rank tensor regression"
    - "polynomial sized topological approximations using the permutahedron"
    - "tensor spectral clustering for partitioning higher order network structures"
    - "pagerank beyond the web"
    - "graph induced complex on point data"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "computing topological persistence for simplicial maps"
    - "applications of zigzag persistence to topological data analysis"

Connected through hyperedge HE-3 (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 approximate multicommodity flow using quadratically coupled flows"
    - "faster spectral sparsification and numerical algorithms for sdd matrices"

Connected through hyperedge HE-4 (induced by source paper "is nearly linear the same in theory and practice a case study with a combinatorial laplacian solver"):
  with members:
    - "simple parallel and distributed algorithms for spectral graph sparsification"
    - "networkit an interactive tool suite for high performance network analysis"
    - "networkit a tool suite for large scale complex network analysis"
    - "an efficient parallel solver for sdd linear systems"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "faster spectral sparsification and numerical algorithms for sdd matrices"
    - "a nearly mlogn time solver for sdd linear systems"
    - "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs"

Connected through hyperedge HE-5 (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"
    - "path finding ii an o m sqrt n algorithm for the minimum cost flow problem"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "evaluating the potential of a dual randomized kaczmarz solver for laplacian linear systems"):
  with members:
    - "is nearly linear the same in theory and practice a case study with a combinatorial laplacian solver"
    - "a scalable generative graph model with community structure"
    - "a simple combinatorial algorithm for solving sdd systems in nearly linear time"
    - "approaching optimality for solving sdd systems"
    - "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs"

Connected through hyperedge HE-7 (induced by source paper "a fast distributed solver for symmetric diagonally dominant linear equations"):
  with members:
    - "solving systems of linear equations locally and asynchronously"
    - "near linear work parallel sdd solvers low diameter decomposition and low stretch subgraphs"
    - "approaching optimality for solving sdd systems"
    - "faster generation of random spanning trees"
    - "nearly linear time algorithms for preconditioning and solving symmetric diagonally dominant linear systems"

Connected through hyperedge HE-8 (induced by source paper "an efficient parallel solver for sdd linear systems"):
  with members:
    - "efficient accelerated coordinate descent methods and faster algorithms for solving 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 approximate multicommodity flow using quadratically coupled flows"
    - "faster spectral sparsification and numerical algorithms for sdd matrices"
    - "near linear work parallel sdd solvers low diameter decomposition and low stretch subgraphs"
    - "a nearly mlogn time solver for sdd linear systems"
    - "approximating the exponential the lanczos method and an tilde o m time spectral algorithm for balanced separator"

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.NA(Numerical Analysis)