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: "optimization and parallelization of b spline based orbital evaluations in qmc on multi many core shared memory processors"
  Abstract: B-spline based orbital representations are widely used in Quantum Monte Carlo (QMC) simulations of solids, historically taking as much as 50% of the total run time. Random accesses to a large four-dimensional array make it challenging to efficiently utilize caches and wide vector units of modern CPUs. We present node-level optimizations of B-spline evaluations on multi/many-core shared memory processors. To increase SIMD efficiency and bandwidth utilization, we first apply data layout transformation from array-of-structures to structure-of-arrays (SoA). Then by blocking SoA objects, we optimize cache reuse and get sustained throughput for a range of problem sizes. We implement efficient nested threading in B-spline orbital evaluation kernels, paving the way towards enabling strong scaling of QMC simulations. These optimizations are portable on four distinct cache-coherent architectures and result in up to 5.6x performance enhancements on Intel Xeon Phi processor 7250P (KNL), 5.7x on Intel Xeon Phi coprocessor 7120P, 10x on an Intel Xeon processor E5v4 CPU and 9.5x on BlueGene/Q processor. Our nested threading implementation shows nearly ideal parallel efficiency on KNL up to 16 threads. We employ roofline performance analysis to model the impacts of our optimizations. This work combined with our current efforts of optimizing other QMC kernels, result in greater than 4.5x speedup of miniQMC on KNL.

Connected through hyperedge HE-1 (induced by source paper "honing and proofing astrophysical codes on the road to exascale experiences from code modernization on many core systems"):
  with members:
    - "energy efficiency features of the intel skylake sp processor and their impact on performance"
    - "performance optimisation of smoothed particle hydrodynamics algorithms for multi many core architectures"
    - "swift using task based parallelism fully asynchronous communication and graph partition based domain decomposition for strong scaling on more than 100 000 cores"

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)