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: "beyond profiling scaling profiling data usage to multiple applications"
  Abstract: Profiling techniques are used extensively at different parts of the computing stack to achieve many goals. One major goal is to make a piece of software execute more efficiently on a specific hardware platform, where efficiency spans criteria such as power, performance, resource requirements, etc. Researchers, both in academia and industry, have introduced many techniques to gather, and make use of, profiling data. However, one thing remains unchanged: making application A run more efficiently on machine 1. In this paper, we extend this criteria by asking: can profiling information of application A on machine 1 be used to make application B run more efficiently on machine 1? If so, then this means as machine 1 continues to execute more applications, it becomes better and more efficient. We present a generalized method for using profiling information gathered from the execution of programs from a limited corpus of applications to improve the performance of software from outside our corpus. As a proof of concept, we apply our technique to the specific problem of selecting the most efficient last-level-cache with which to execute an application. We were able to turn off an average of 19% of last-level-cache blocks for selected programs from PARSEC benchmark suite and only saw an average 2.8% increase in the rate of last-level cache misses.

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)