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 "chebyshev filter diagonalization on modern manycore processors and gpgpus"):
  Abstract: Chebyshev filter diagonalization is well established in quantum chemistry and quantum physics to compute bulks of eigenvalues of large sparse matrices. Choosing a block vector implementation, we investigate optimization opportunities on the new class of high-performance compute devices featuring both high-bandwidth and low-bandwidth memory. We focus on the transparent access to the full address space supported by both architectures under consideration: Intel Xeon Phi “Knights Landing” and Nvidia “Pascal”/“Volta.” After a thorough performance analysis of the single-device implementations using the roofline model we propose two optimizations: (1) Subspace blocking is applied for improved performance and data access efficiency. We also show that it allows transparently handling problems much larger than the high-bandwidth memory without significant performance penalties. (2) Pipelining of communication and computation phases of successive subspaces is implemented to hide communication costs without extra memory traffic. As an application scenario we perform filter diagonalization studies for topological quantum matter. Performance numbers on up to 2048 nodes of the Oakforest-PACS and Piz Daint supercomputers are presented, achieving beyond 500 Tflop/s for computing \(10^2\) inner eigenvalues of sparse matrices of dimension \(4\cdot 10^9\).

Includes members:
  Member "ghost building blocks for high performance sparse linear algebra on heterogeneous systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "performance engineering for real and complex tall skinny matrix multiplication kernels on gpus")
  Member "performance engineering of the kernel polynomal method on large scale cpu gpu systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "desynchronization and wave pattern formation in mpi parallel and hybrid memory bound programs")
  Member "a unified sparse matrix data format for efficient general sparse matrix vector multiplication on modern processors with wide simd units":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "characterizing scalability of sparse matrix vector multiplications on phytium ft 2000")
  Member "efficient estimation of eigenvalue counts in an interval":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "approximating spectral clustering via sampling a review")
  Member "sparse matrix vector multiplication on gpgpu clusters a new storage format and a scalable implementation":
    also connected through other hyperedges:
      (none)
  Member "likwid a lightweight performance oriented tool suite for x86 multicore environments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "lbmpy a flexible code generation toolkit for highly efficient lattice boltzmann simulations")
  Member "likwid a lightweight performance oriented tool suite for x86 multicore environments":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lbmpy a flexible code generation toolkit for highly efficient lattice boltzmann simulations")

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)