human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "the blas api of blasfeo optimizing performance for small matrices"):
  Abstract: BLASFEO is a dense linear algebra library providing high-performance implementations of BLAS- and LAPACK-like routines for use in embedded optimization, and is therefore targeting relatively small matrices. One of the key features of BLASFEO is the use of a packed matrix format as the native format in its API. For matrices fitting in cache, this matrix format is analogous to the packed sub-matrices in the memory buffers of optimized BLAS libraries, but with the key advantage of removing the packing cost from the routine call. Thanks to that, for small matrices BLASFEO outperforms optimized BLAS implementations, both open source and proprietary, on a wide range of computer architectures. This paper investigates the addition of a standard BLAS API to the BLASFEO framework, and proposes an implementation switching between two algorithms, which are optimized for different matrix sizes. By leveraging the modular nature of the assembly kernels framework in BLASFEO, tailored linear algebra kernels with mixed column- and panel-major arguments are easily developed. This BLAS API has generally lower performance than the BLASFEO API, but it nonetheless outperforms optimized BLAS and especially LAPACK libraries for matrices fitting in cache. Therefore, it can be used to boost a wide range of applications, where standard BLAS and LAPACK libraries are employed and the matrix size is moderate. In particular, this paper investigates the benefits in scientific programming languages such as Octave, Python SciPy and Julia.

Includes members:
  Member "embedded online optimization for model predictive control at megahertz rates":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "large scale model predictive control with neural networks and primal active sets")
  Member "a class of parallel tiled linear algebra algorithms for multicore architectures":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "task based gpu accelerated and robust library for solving dense nonsymmetric eigenvalue problems")

Question: Please tell me which class the source paper 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)