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: "performance modeling for dense linear algebra"
  Abstract: It is well known that the behavior of dense linear algebra algorithms is greatly influenced by factors like target architecture, underlying libraries and even problem size; because of this, the accurate prediction of their performance is a real challenge. In this article, we are not interested in creating accurate models for a given algorithm, but in correctly ranking a set of equivalent algorithms according to their performance. Aware of the hierarchical structure of dense linear algebra routines, we approach the problem by developing a framework for the automatic generation of statistical performance models for BLAS and LAPACK libraries. This allows us to obtain predictions through evaluating and combining such models. We demonstrate that our approach is successful in both single- and multi-core environments, not only in the ranking of algorithms but also in tuning their parameters.

Connected through hyperedge HE-1 (induced by source paper "linnea automatic generation of efficient linear algebra programs"):
  with members:
    - "the linear algebra mapping problem"
    - "automatic generation of efficient linear algebra programs"
    - "the generalized matrix chain algorithm"
    - "non linear associative commutative many to one pattern matching with sequence variables"
    - "efficient pattern matching in python"
    - "matchpy a pattern matching library"
    - "sparsity based correction of exponential artifacts"
    - "a parallel implementation of the ensemble kalman filter based on modified cholesky decomposition"

Connected through hyperedge HE-2 (induced by source paper "yet another tensor toolbox for discontinuous galerkin methods and other applications"):
  with members:
    - "design of a high performance gemm like tensor tensor multiplication"
    - "high performance tensor contraction without transposition"
    - "madness a multiresolution adaptive numerical environment for scientific simulation"
    - "tensor contractions with extended blas kernels on cpu and gpu"
    - "cross loop optimization of arithmetic intensity for finite element local assembly"
    - "towards an efficient use of the blas library for multilinear tensor contractions"

Connected through hyperedge HE-3 (induced by source paper "the generalized matrix chain algorithm"):
  with members:
    - "non linear associative commutative many to one pattern matching with sequence variables"
    - "deriving correct high performance algorithms"
    - "a dual method for computing power transfer distribution factors"
    - "sparsity based correction of exponential artifacts"
    - "a parallel implementation of the ensemble kalman filter based on modified cholesky decomposition"
    - "consistent discretization and minimization of the l1 norm on manifolds"
    - "the elaps framework experimental linear algebra performance studies"
    - "on a natural dynamics for linear programming"

Connected through hyperedge HE-4 (induced by source paper "design of a high performance gemm like tensor tensor multiplication"):
  with members:
    - "hptt a high performance tensor transposition c library"
    - "ttc a high performance compiler for tensor transpositions"
    - "madness a multiresolution adaptive numerical environment for scientific simulation"
    - "ttc a tensor transposition compiler for multiple architectures"
    - "tensor contractions with extended blas kernels on cpu and gpu"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "fast convolutional nets with fbfft a gpu performance evaluation"
    - "cudnn efficient primitives for deep learning"

Connected through hyperedge HE-5 (induced by source paper "tensor contractions with extended blas kernels on cpu and gpu"):
  with members:
    - "on the performance prediction of blas based tensor contractions"
    - "a gemm interface and implementation on nvidia gpus for multiple small matrices"
    - "fourier pca and robust tensor decomposition"
    - "towards an efficient use of the blas library for multilinear tensor contractions"
    - "tensor decompositions for learning latent variable models"

Connected through hyperedge HE-6 (induced by source paper "the elaps framework experimental linear algebra performance studies"):
  with members:
    - "on the performance prediction of blas based tensor contractions"
    - "solving sequences of generalized least squares problems on multi threaded architectures"
    - "computing petaflops over terabytes of data the case of genome wide association studies"

Connected through hyperedge HE-7 (induced by source paper "on the performance prediction of blas based tensor contractions"):
  with members:
    - "towards an efficient use of the blas library for multilinear tensor contractions"

Connected through hyperedge HE-8 (induced by source paper "application tailored linear algebra algorithms a search based approach"):
  with members:
    - "high throughput genome wide association analysis for single and multiple phenotypes"
    - "computing petaflops over terabytes of data the case of genome wide association studies"
    - "dolfin automated finite element computing"

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)