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: "automatic performance debugging of spmd parallel programs"
  Abstract: Automatic performance debugging of parallel applications usually involves two steps: automatic detection of performance bottlenecks and uncovering their root causes for performance optimization. Previous work fails to resolve this challenging issue in several ways: first, several previous efforts automate analysis processes, but present the results in a confined way that only identifies performance problems with apriori knowledge; second, several tools take exploratory or confirmatory data analysis to automatically discover relevant performance data relationships. However, these efforts do not focus on locating performance bottlenecks or uncovering their root causes. In this paper, we design and implement an innovative system, AutoAnalyzer, to automatically debug the performance problems of single program multi-data (SPMD) parallel programs. Our system is unique in terms of two dimensions: first, without any apriori knowledge, we automatically locate bottlenecks and uncover their root causes for performance optimization; second, our method is lightweight in terms of size of collected and analyzed performance data. Our contribution is three-fold. First, we propose a set of simple performance metrics to represent behavior of different processes of parallel programs, and present two effective clustering and searching algorithms to locate bottlenecks. Second, we propose to use the rough set algorithm to automatically uncover the root causes of bottlenecks. Third, we design and implement the AutoAnalyzer system, and use two production applications to verify the effectiveness and correctness of our methods. According to the analysis results of AutoAnalyzer, we optimize two parallel programs with performance improvements by minimally 20% and maximally 170%.

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)