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 note on preconditioning by low stretch spanning trees"
  Abstract: Boman and Hendrickson observed that one can solve linear systems in Laplacian matrices in time $\bigO{m^{3/2 + o (1)} \ln (1/\epsilon)}$ by preconditioning with the Laplacian of a low-stretch spanning tree. By examining the distribution of eigenvalues of the preconditioned linear system, we prove that the preconditioned conjugate gradient will actually solve the linear system in time $\softO{m^{4/3} \ln (1/\epsilon)}$.

Connected through hyperedge HE-1 (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"

Connected through hyperedge HE-2 (induced by source paper "grass spectral sparsification leveraging scalable spectral perturbation analysis"):
  with members:
    - "similarity aware spectral sparsification by edge filtering"
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "almost linear time algorithms for markov chains and new spectral primitives for directed graphs"
    - "partitioning well clustered graphs spectral clustering works"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs"
    - "subgraph sparsification and nearly optimal ultrasparsifiers"

Connected through hyperedge HE-3 (induced by source paper "nearly linear time spectral graph reduction for scalable graph partitioning and data visualization"):
  with members:
    - "spectrally approximating large graphs with smaller graphs"
    - "clustering with t sne provably"
    - "almost linear time algorithms for markov chains and new spectral primitives for directed graphs"
    - "approximate spectral clustering efficiency and guarantees"
    - "partitioning well clustered graphs spectral clustering works"
    - "recent advances in graph partitioning"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "community detection and graph partitioning"

Connected through hyperedge HE-4 (induced by source paper "similarity aware spectral sparsification by edge filtering"):
  with members:
    - "an sdp based algorithm for linear sized spectral sparsification"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "almost linear time algorithms for markov chains and new spectral primitives for directed graphs"
    - "partitioning well clustered graphs spectral clustering works"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "electrical flows laplacian systems and faster approximation of maximum flow in undirected graphs"
    - "twice ramanujan sparsifiers"
    - "spectral sparsification of graphs"

Connected through hyperedge HE-5 (induced by source paper "stability of the lanczos method for matrix function approximation"):
  with members:
    - "spectrum approximation beyond fast matrix multiplication algorithms and hardness"
    - "lazysvd even faster svd decomposition yet without agonizing pain"
    - "faster algorithms for computing the stationary distribution simulating random walks and more"
    - "principal component projection without principal component analysis"
    - "randomized block krylov methods for stronger and faster approximate singular value decomposition"
    - "large scale log determinant computation through stochastic chebyshev expansions"
    - "the noisy power method a meta algorithm with applications"
    - "approximating the exponential the lanczos method and an tilde o m time spectral algorithm for balanced separator"

Connected through hyperedge HE-6 (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-7 (induced by source paper "duality and nonlinear graph laplacians"):
  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"
    - "graph sparsification by effective resistances"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving linear systems"

Connected through hyperedge HE-8 (induced by source paper "preconditioning in expectation"):
  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"
    - "a matrix hyperbolic cosine algorithm and applications"
    - "randomized algorithms for matrices and data"
    - "nearly tight low stretch spanning trees"
    - "nearly linear time algorithms for preconditioning and solving symmetric diagonally dominant linear systems"
    - "nearly linear time algorithms for graph partitioning graph sparsification and solving 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.NA(Numerical Analysis)