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: "approximation of bem matrices using gpgpus"
  Abstract: The efficiency of boundary element methods depends crucially on the time required for setting up the stiffness matrix. The far-field part of the matrix can be approximated by compression schemes like the fast multipole method or $\mathcal{H}$-matrix techniques. The near-field part is typically approximated by special quadrature rules like the Sauter-Schwab technique that can handle the singular integrals appearing in the diagonal and near-diagonal matrix elements. #R##N#Since computing one element of the matrix requires only a small amount of data but a fairly large number of operations, we propose to use GPUs to handle vectorizable portions of the computation: near-field computations are ideally suited for vectorization and can therefore be handled very well by GPUs. Modern far-field compression schemes can be split into a small adaptive portion that exhibits divergent control flows and is handled by the CPU and a vectorizable portion that can again be sent to GPUs. #R##N#We propose a hybrid algorithm that splits the computation into tasks for CPUs and GPUs. Our method presented in this article is able to speedup the setup time of boundary integral operators by a significant factor of 19-30 for both the Laplace and the Helmholtz equation in 3D when using two consumer GPGPUs compared to a quad-core CPU.

Connected through hyperedge HE-1 (induced by source paper "hierarchical matrix operations on gpus matrix vector multiplication and compression"):
  with members:
    - "algorithmic patterns for mathcal h matrices on many core processors"
    - "batched qr and svd algorithms on gpus with applications in hierarchical matrix compression"
    - "kblas an optimized library for dense matrix vector multiplication on gpu accelerators"
    - "communication complexity of the fast multipole method and its algebraic variants"

Connected through hyperedge HE-2 (induced by source paper "a scalable h matrix approach for the solution of boundary integral equations on multi gpu clusters"):
  with members:
    - "batched qr and svd algorithms on gpus with applications in hierarchical matrix compression"

Connected through hyperedge HE-3 (induced by source paper "parallel approximation of the maximum likelihood estimation for the prediction of large scale geostatistics simulations"):
  with members:
    - "exageostat a high performance unified framework for geostatistics on manycore systems"

Connected through hyperedge HE-4 (induced by source paper "algorithmic patterns for mathcal h matrices on many core processors"):
  with members:
    - "a distributed memory package for dense hierarchically semi separable matrix computations using randomization"
    - "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"

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)