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: "comparison of different propagation steps for lattice boltzmann methods"
  Abstract: Several possibilities exist to implement the propagation step of lattice Boltzmann methods. This paper describes common implementations and compares the number of memory transfer operations they require per lattice node update. A performance model based on the memory bandwidth is then used to obtain an estimation of the maximum achievable performance on different machines. A subset of the discussed implementations of the propagation step are benchmarked on different Intel- and AMD-based compute nodes using the framework of an existing flow solver that is specially adapted to simulate flow in porous media, and the model is validated against the measurements. Advanced approaches for the propagation step like ''A-A pattern'' or ''Esoteric Twist'' require more programming effort but often sustain significantly better performance than non-naive but straightforward implementations.

Connected through hyperedge HE-1 (induced by source paper "lbmpy a flexible code generation toolkit for highly efficient lattice boltzmann simulations"):
  with members:
    - "walberla a block structured high performance framework for multiphysics simulations"
    - "lattice boltzmann benchmark kernels as a testbed for performance analysis"
    - "a python extension for the massively parallel multiphysics simulation framework walberla"
    - "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-2 (induced by source paper "lattice boltzmann benchmark kernels as a testbed for performance analysis"):
  with members:
    - "modeling and analyzing performance for highly optimized propagation steps of the lattice boltzmann method on sparse lattices"
    - "chip level and multi node analysis of energy optimized lattice boltzmann cfd simulations"
    - "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-3 (induced by source paper "early experience on using knights landing processors for lattice boltzmann applications"):
  with members:
    - "evaluation of dvfs techniques on modern hpc processors and accelerators for energy aware applications"
    - "first experiences optimizing smith waterman on intel s knights landing processor"
    - "optimization of lattice boltzmann simulations on heterogeneous computers"
    - "massively parallel lattice boltzmann codes on large gpu clusters"

Connected through hyperedge HE-4 (induced by source paper "optimization of lattice boltzmann simulations on heterogeneous computers"):
  with members:
    - "performance and portability of accelerated lattice boltzmann applications with openacc"
    - "massively parallel lattice boltzmann codes on large gpu clusters"

Connected through hyperedge HE-5 (induced by source paper "massively parallel lattice boltzmann codes on large gpu clusters"):
  with members:
    - "performance and portability of accelerated lattice boltzmann applications with openacc"

Connected through hyperedge HE-6 (induced by source paper "a progressive mesh method for physical simulations using lattice boltzmann method on single node multi gpu architectures"):
  with members:
    - "performance engineering for the lattice boltzmann method on gpgpus architectural requirements and performance results"

Connected through hyperedge HE-7 (induced by source paper "modeling and analyzing performance for highly optimized propagation steps of the lattice boltzmann method on sparse lattices"):
  with members:
    - "domain decomposition and locality optimization for large scale lattice boltzmann simulations"
    - "chip level and multi node analysis of energy optimized lattice boltzmann cfd simulations"

Connected through hyperedge HE-8 (induced by source paper "chip level and multi node analysis of energy optimized lattice boltzmann cfd simulations"):
  with members:
    - "pushing the limits for medical image reconstruction on recent standard multicore processors"
    - "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"

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.DC(Distributed, Parallel, and Cluster Computing)