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 distributed memory package for dense hierarchically semi separable matrix computations using randomization"
  Abstract: We present a distributed-memory library for computations with dense structured matrices. A matrix is considered structured if its off-diagonal blocks can be approximated by a rank-deficient matrix with low numerical rank. Here, we use Hierarchically Semi-Separable representations (HSS). Such matrices appear in many applications, e.g., finite element methods, boundary element methods, etc. Exploiting this structure allows for fast solution of linear systems and/or fast computation of matrix-vector products, which are the two main building blocks of matrix computations. The compression algorithm that we use, that computes the HSS form of an input dense matrix, relies on randomized sampling with a novel adaptive sampling mechanism. We discuss the parallelization of this algorithm and also present the parallelization of structured matrix-vector product, structured factorization and solution routines. The efficiency of the approach is demonstrated on large problems from different academic and industrial applications, on up to 8,000 cores. #R##N#This work is part of a more global effort, the STRUMPACK (STRUctured Matrices PACKage) software package for computations with sparse and dense structured matrices. Hence, although useful on their own right, the routines also represent a step in the direction of a distributed-memory sparse solver.

Connected through hyperedge HE-1 (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 fast block low rank dense solver with applications to finite element matrices"
    - "far field compression for fast kernel summation methods in high dimensions"
    - "askit approximate skeletonization kernel independent treecode in high dimensions"

Connected through hyperedge HE-2 (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 fast block low rank dense solver with applications to finite element matrices"
    - "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-3 (induced by source paper "a study of clustering techniques and hierarchical matrix formats for kernel ridge regression"):
  with members:
    - "on the numerical rank of radial basis function kernels in high dimensions"
    - "far field compression for fast kernel summation methods in high dimensions"
    - "askit approximate skeletonization kernel independent treecode in high dimensions"
    - "revisiting the nystrom method for improved large scale machine learning"
    - "sharp analysis of low rank kernel matrix approximations"
    - "randomized algorithms for matrices and data"

Connected through hyperedge HE-4 (induced by source paper "an n log n parallel fast direct solver for kernel matrices"):
  with members:
    - "scalable kernel methods via doubly stochastic gradients"
    - "how to scale up kernel methods to be as good as deep neural nets"
    - "askit approximate skeletonization kernel independent treecode in high dimensions"
    - "revisiting the nystrom method for improved large scale machine learning"

Connected through hyperedge HE-5 (induced by source paper "geometry oblivious fmm for compressing dense spd matrices"):
  with members:
    - "an n log n parallel fast direct solver for kernel matrices"
    - "inv askit a parallel fast diret solver for kernel matrices"
    - "fast multipole method as a matrix free hierarchical low rank approximation"
    - "mlpack a scalable c machine learning library"

Connected through hyperedge HE-6 (induced by source paper "algorithmic patterns for mathcal h matrices on many core processors"):
  with members:
    - "approximation of bem matrices using gpgpus"
    - "fmm based vortex method for simulation of isotropic turbulence on gpus compared with a spectral method"
    - "petrbf a parallel o n algorithm for radial basis function interpolation with gaussians"

Connected through hyperedge HE-7 (induced by source paper "fast multipole method as a matrix free hierarchical low rank approximation"):
  with members:
    - "a fast block low rank dense solver with applications to finite element matrices"
    - "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-8 (induced by source paper "an efficient hybrid tridiagonal divide and conquer algorithm on distributed memory architectures"):
  with members:
    - "new fast divide and conquer algorithms for the symmetric tridiagonal eigenvalue problem"
    - "fast algorithms for spherical harmonic expansions"

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.MS(Mathematical Software)