human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "pushing the limits for medical image reconstruction on recent standard multicore processors"
  Abstract: Volume reconstruction by backprojection is the computational bottleneck in many interventional clinical computed tomography (CT) applications. Today vendors in this field replace special purpose hardware accelerators with standard hardware such as multicore chips and GPGPUs. Medical imaging algorithms are on the verge of employing high-performance computing (HPC) technology, and are therefore an interesting new candidate for optimization. This paper presents low-level optimizations for the backprojection algorithm, guided by a thorough performance analysis on four generations of Intel multicore processors (Harpertown, Westmere, Westmere EX, and Sandy Bridge). We choose the RabbitCT benchmark, a standardized testcase well supported in industry, to ensure transparent and comparable results. Our aim is to provide not only the fastest possible implementation but also compare with performance models and hardware counter data in order to fully understand the results. We separate the influence of algorithmic optimizations, parallelization, SIMD vectorization, and microarchitectural issues and pinpoint problems with current SIMD instruction set extensions on standard CPUs (SSE, AVX). The use of assembly language is mandatory for best performance. Finally, we compare our results to the best GPGPU implementations available for this open competition benchmark.

Connected through hyperedge HE-1 (induced by source paper "ifdk a scalable framework for instant high resolution image reconstruction"):
  with members:
    - "evaluating modern gpu interconnect pcie nvlink nv sli nvswitch and gpudirect"
    - "comparing the performance of different x86 simd instruction sets for a medical imaging application on modern multi and manycore chips"
    - "performance engineering for a medical imaging application on the intel xeon phi accelerator"

Connected through hyperedge HE-2 (induced by source paper "an analysis of core and chip level architectural features in four generations of intel server processors"):
  with members:
    - "an ecm based energy efficiency optimization approach for bandwidth limited streaming kernels on recent intel xeon processors"
    - "analysis of intel s haswell microarchitecture using the ecm model and microbenchmarks"
    - "quantifying performance bottlenecks of stencil computations using the execution cache memory model"
    - "comparing the performance of different x86 simd instruction sets for a medical imaging application on modern multi and manycore chips"
    - "chip level and multi node analysis of energy optimized lattice boltzmann cfd simulations"
    - "exploring performance and power properties of modern multicore chips via simple machine models"

Connected through hyperedge HE-3 (induced by source paper "comparing the performance of different x86 simd instruction sets for a medical imaging application on modern multi and manycore chips"):
  with members:
    - "likwid a lightweight performance oriented tool suite for x86 multicore environments"
    - "likwid a lightweight performance oriented tool suite for x86 multicore environments"

Connected through hyperedge HE-4 (induced by source paper "chip level and multi node analysis of energy optimized lattice boltzmann cfd simulations"):
  with members:
    - "comparison of different propagation steps for lattice boltzmann methods"
    - "exploring performance and power properties of modern multicore chips via simple machine models"
    - "leveraging shared caches for parallel temporal blocking of stencil codes on multicore processors and clusters"
    - "introducing a performance model for bandwidth limited loop kernels"

Connected through hyperedge HE-5 (induced by source paper "performance engineering for a medical imaging application on the intel xeon phi accelerator"):
  with members:
    - "likwid a lightweight performance oriented tool suite for x86 multicore environments"
    - "likwid a lightweight performance oriented tool suite for x86 multicore environments"
    - "introducing a performance model for bandwidth limited loop kernels"

Connected through hyperedge HE-6 (induced by source paper "performance patterns and hardware metrics on modern multicore processors best practices for performance engineering"):
  with members:
    - "expression templates revisited a performance analysis of current methodologies"
    - "likwid a lightweight performance oriented tool suite for x86 multicore environments"
    - "likwid a lightweight performance oriented tool suite for x86 multicore environments"

Connected through hyperedge HE-7 (induced by source paper "exploring performance and power properties of modern multicore chips via simple machine models"):
  with members:
    - "likwid a lightweight performance oriented tool suite for x86 multicore environments"
    - "likwid a lightweight performance oriented tool suite for x86 multicore environments"
    - "introducing a performance model for bandwidth limited loop kernels"

Question: Please tell me which class the center node 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)