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 "algorithms and data structures for matrix free finite element operators with mpi parallel sparse multi vectors"):
  Abstract: Traditional solution approaches for problems in quantum mechanics scale as $\mathcal O(M^3)$, where $M$ is the number of electrons. Various methods have been proposed to address this issue and obtain linear scaling $\mathcal O(M)$. One promising formulation is the direct minimization of energy. Such methods take advantage of physical localization of the solution, namely that the solution can be sought in terms of non-orthogonal orbitals with local support. In this work a numerically efficient implementation of sparse parallel vectors within the open-source finite element library deal.II is proposed. The main algorithmic ingredient is the matrix-free evaluation of the Hamiltonian operator by cell-wise quadrature. Based on an a-priori chosen support for each vector we develop algorithms and data structures to perform (i) matrix-free sparse matrix multivector products (SpMM), (ii) the projection of an operator onto a sparse sub-space (inner products), and (iii) post-multiplication of a sparse multivector with a square matrix. The node-level performance is analyzed using a roofline model. Our matrix-free implementation of finite element operators with sparse multivectors achieves the performance of 157 GFlop/s on Intel Cascade Lake architecture. Strong and weak scaling results are reported for a typical benchmark problem using quadratic and quartic finite element bases.

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 "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-2 (induced by source paper "characterizing scalability of sparse matrix vector multiplications on phytium ft 2000")
  Member "an optimized sparse approximate matrix multiply for matrices with decay":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "locality aware parallel block sparse matrix matrix multiplication using the chunks and tasks programming model")
  Member "likwid a lightweight performance oriented tool suite for x86 multicore environments":
    also connected through other hyperedges:
      - Hyperedge HE-4 (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-5 (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)