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: "10 millisecond computing"
  Abstract: Despite computation becomes much complex on data with unprecedented large-scale, we argue computers or smart devices should and will consistently provide information and knowledge to human being in the order of a few tens milliseconds. We coin a new term 10-millisecond computing to call attention to this class of workloads. Public reports indicate that internet service users are sensitive to the service or job-level response time outliers, so we propose a very simple but powerful metric-outlier proportion to characterize the system behaviors.The outlier proportion is defined as follows: for N completed requests or jobs, if M jobs or requests' latencies exceed the outlier limit t, e.g. 10 milliseconds, the outlier proportion is M/N. 10-millisecond computing raises many challenges for both software and hardware stacks. In this paper, as a case study we investigate the challenges raised for conventional operating systems. For typical latency-critical services running with Linux on a 40-core server - a main-stream server hardware system in near future, we found, when the outlier limit decreases, the outlier proportion of a single server will significantly deteriorate. Meanwhile, the outlier proportion is further amplified by the system scale, including the system core number. For a 1K-scale system, we surprisingly find that to reduce the service or job-level outlier proportion to 10%, running Linux (version 2.6.32) or LXC (version 0.7.5 ) or XEN (version 4.0.0), respectively, the outlier proportion of a single server needs to be reduced by 871X, 2372X, 2372X accordingly. We also conducted a list of experiments to reveal the current Linux systems still suffer from poor outlier performance, including Linux kernel version 3.17.4, Linux kernel version 2.6.35M, a modified version of 2.6.35 integrated with sloppy counters and two representative real time schedulers.

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)