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 "ghost building blocks for high performance sparse linear algebra on heterogeneous systems"):
  Abstract: While many of the architectural details of future exascale-class high performance computer systems are still a matter of intense research, there appears to be a general consensus that they will be strongly heterogeneous, featuring “standard” as well as “accelerated” resources. Today, such resources are available as multicore processors, graphics processing units (GPUs), and other accelerators such as the Intel Xeon Phi. Any software infrastructure that claims usefulness for such environments must be able to meet their inherent challenges: massive multi-level parallelism, topology, asynchronicity, and abstraction. The “General, Hybrid, and Optimized Sparse Toolkit” (GHOST) is a collection of building blocks that targets algorithms dealing with sparse matrix representations on current and future large-scale systems. It implements the “MPI+X” paradigm, has a pure C interface, and provides hybrid-parallel numerical kernels, intelligent resource management, and truly heterogeneous parallelism for multicore CPUs, Nvidia GPUs, and the Intel Xeon Phi. We describe the details of its design with respect to the challenges posed by modern heterogeneous supercomputers and recent algorithmic developments. Implementation details which are indispensable for achieving high efficiency are pointed out and their necessity is justified by performance measurements or predictions based on performance models. We also provide instructions on how to make use of GHOST in existing software packages, together with a case study which demonstrates the applicability and performance of GHOST as a component within a larger software stack. The library code and several applications are available as open source.

Includes members:
  Member "pipelined iterative solvers with kernel fusion for graphics processing units":
    also connected through other hyperedges:
      (none)
  Member "performance engineering of the kernel polynomal method on large scale cpu gpu systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "characterizing scalability of sparse matrix vector multiplications on phytium ft 2000")
  Member "asynchronous mpi for the masses":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a high level characterisation and generalisation of communication avoiding programming techniques")
  Member "density matrix based algorithm for solving eigenvalue problems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "chase chebyshev accelerated subspace iteration eigensolver for sequences of hermitian eigenvalue problems")
  Member "pt scotch a tool for efficient parallel graph ordering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an optimized parallel computation of the ghost layer for adaptive hybrid forest meshes")

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.DC(Distributed, Parallel, and Cluster Computing)