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: "finally how many efficiencies the supercomputers have and what do they measure"
  Abstract: In the "gold rush" for higher performance numbers, a lot of confusion was introduced in supercomputing. The present paper attempts to clear up the terms through scrutinizing the basic terms, contributions, measurement methods. It is shown that using extremely large number of processing elements in computing systems leads to unexpected phenomena, that cannot be explained in the frame of the classical computing paradigm. The phenomena show interesting parallels with the phenomena experienced in science more than a century ago and through their studying a modern science was introduced. The introduced simple non-technical model enables to set up a frame and formalism enabling to explain the unexplained experiences around supercomputing. The model also enables to derive predictions of supercomputer performance for the near future as well as provides hints for enhancing supercomputer components.

Connected through hyperedge HE-1 (induced by source paper "re evaluating scaling methods for distributed parallel systems"):
  with members:
    - "the need for modern computing paradigm science applied to computing"
    - "comparative benchmarking of cloud computing vendors with high performance linpack"
    - "a configurable accelerator for manycores the explicitly many processor approach"

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)