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 "analytic performance modeling and analysis of detailed neuron simulations"):
  Abstract: Big science initiatives are trying to reconstruct and model the brain by attempting to simulate brain tissue at larger scales and with increasingly more biological detail than previously thought possible. The exponential growth of parallel computer performance has been supporting these developments, and at the same time maintainers of neuroscientific simulation code have strived to optimally and efficiently exploit new hardware features. Current state of the art software for the simulation of biological networks has so far been developed using performance engineering practices, but a thorough analysis and modeling of the computational and performance characteristics, especially in the case of morphologically detailed neuron simulations, is lacking. Other computational sciences have successfully used analytic performance engineering and modeling methods to gain insight on the computational properties of simulation kernels, aid developers in performance optimizations and eventually drive co-design efforts, but to our knowledge a model-based performance analysis of neuron simulations has not yet been conducted. #R##N#We present a detailed study of the shared-memory performance of morphologically detailed neuron simulations based on the Execution-Cache-Memory (ECM) performance model. We demonstrate that this model can deliver accurate predictions of the runtime of almost all the kernels that constitute the neuron models under investigation. The gained insight is used to identify the main governing mechanisms underlying performance bottlenecks in the simulation. The implications of this analysis on the optimization of neural simulation software and eventually co-design of future hardware architectures are discussed. In this sense, our work represents a valuable conceptual and quantitative contribution to understanding the performance properties of biological networks simulations.

Includes members:
  Member "on the accuracy and usefulness of analytic energy models for contemporary multicore processors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "collecting and presenting reproducible intranode stencil performance inspect")
  Member "automated instruction stream throughput prediction for intel and amd microarchitectures":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "smotherspectre exploiting speculative execution through port contention")
  Member "an analysis of core and chip level architectural features in four generations of intel server processors":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "understanding hpc benchmark performance on intel broadwell and cascade lake processors")
  Member "kerncraft a tool for analytic performance modeling of loop kernels":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "collecting and presenting reproducible intranode stencil performance inspect")
  Member "quantifying performance bottlenecks of stencil computations using the execution cache memory model":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "desynchronization and wave pattern formation in mpi parallel and hybrid memory bound programs")
  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")
  Member "likwid a lightweight performance oriented tool suite for x86 multicore environments":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lbmpy a flexible code generation toolkit for highly efficient lattice boltzmann simulations")
  Member "introducing a performance model for bandwidth limited loop kernels":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "a mechanism for balancing accuracy and scope in cross machine black box gpu performance modeling")

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.PF(Performance)