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 fast block low rank dense solver with applications to finite element matrices"
  Abstract: This article presents a fast solver for the dense "frontal" matrices that arise from the multifrontal sparse elimination process of 3D elliptic PDEs. The solver relies on the fact that these matrices can be efficiently represented as a hierarchically off-diagonal low-rank (HODLR) matrix. To construct the low-rank approximation of the off-diagonal blocks, we propose a new pseudo-skeleton scheme, the boundary distance low-rank approximation, that picks rows and columns based on the location of their corresponding vertices in the sparse matrix graph. We compare this new low-rank approximation method to the adaptive cross approximation (ACA) algorithm and show that it achieves better speedup specially for unstructured meshes. Using the HODLR direct solver as a preconditioner (with a low tolerance) to the GMRES iterative scheme, we can reach machine accuracy much faster than a conventional LU solver. Numerical benchmarks are provided for frontal matrices arising from 3D finite element problems corresponding to a wide range of applications.

Connected through hyperedge HE-1 (induced by source paper "geostatistical modeling and prediction using mixed precision tile cholesky factorization"):
  with members:
    - "parallel approximation of the maximum likelihood estimation for the prediction of large scale geostatistics simulations"

Connected through hyperedge HE-2 (induced by source paper "matrox modular approach for improving data locality in hierarchical mat rix app rox imation"):
  with members:
    - "a study of clustering techniques and hierarchical matrix formats for kernel ridge regression"
    - "sympiler transforming sparse matrix codes by decoupling symbolic analysis"
    - "geometry oblivious fmm for compressing dense spd matrices"
    - "a distributed memory package for dense hierarchically semi separable matrix computations using randomization"
    - "far field compression for fast kernel summation methods in high dimensions"
    - "askit approximate skeletonization kernel independent treecode in high dimensions"

Connected through hyperedge HE-3 (induced by source paper "matrox a model based algorithm with an efficient storage format for parallel hss structured matrix approximations"):
  with members:
    - "a study of clustering techniques and hierarchical matrix formats for kernel ridge regression"
    - "geometry oblivious fmm for compressing dense spd matrices"
    - "a distributed memory package for dense hierarchically semi separable matrix computations using randomization"
    - "uniform sampling for matrix approximation"
    - "askit approximate skeletonization kernel independent treecode in high dimensions"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "randomized algorithms for matrices and data"

Connected through hyperedge HE-4 (induced by source paper "a direct elliptic solver based on hierarchically low rank schur complements"):
  with members:
    - "an efficient multi core implementation of a novel hss structured multifrontal solver using randomized sampling"

Connected through hyperedge HE-5 (induced by source paper "fast multipole method as a matrix free hierarchical low rank approximation"):
  with members:
    - "a distributed memory package for dense hierarchically semi separable matrix computations using randomization"
    - "askit approximate skeletonization kernel independent treecode in high dimensions"
    - "petascale turbulence simulation using a highly parallel fast multipole method on gpus"
    - "fast multipole preconditioners for sparse matrices arising from elliptic equations"
    - "biomolecular electrostatics using a fast multipole bem on up to 512 gpus and a billion unknowns"

Connected through hyperedge HE-6 (induced by source paper "a distributed memory package for dense hierarchically semi separable matrix computations using randomization"):
  with members:
    - "an efficient multi core implementation of a novel hss structured multifrontal solver using randomized sampling"

Connected through hyperedge HE-7 (induced by source paper "fast multipole preconditioners for sparse matrices arising from elliptic equations"):
  with members:
    - "petiga high performance isogeometric analysis"
    - "an fmm based on dual tree traversal for many core architectures"

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)