human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "augury a time series based application for the analysis and forecasting of system and network performance metrics"):
  Abstract: This paper presents AUGURY, an application for the analysis of monitoring data from computers, servers or cloud infrastructures. The analysis is based on the extraction of patterns and trends from historical data, using elements of time-series analysis. The purpose of AUGURY is to aid a server administrator by forecasting the behaviour and resource usage of specific applications and in presenting a status report in a concise manner. AUGURY provides tools for identifying network traffic congestion and peak usage times, and for making memory usage projections. The application data processing specialises in two tasks: the parametrisation of the memory usage of individual applications and the extraction of the seasonal component from network traffic data. AUGURY uses a different underlying assumption for each of these two tasks. With respect to the memory usage, a limited number of single-valued parameters are assumed to be sufficient to parameterize any application being hosted on the server. Regarding the network traffic data, long-term patterns, such as hourly or daily exist and are being induced by work-time schedules and automatised administrative jobs. In this paper, the implementation of each of the two tasks is presented, tested using locally-generated data, and applied to data from weather forecasting applications hosted on a web server. This data is used to demonstrate the insight that AUGURY can add to the monitoring of server and cloud infrastructures.

Includes members:
  Member "a review of network traffic analysis and prediction techniques":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "artificial intelligence inspired self deployment of wireless networks")
  Member "an introductory study on time series modeling and forecasting":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "towards better forecasting by fusing near and distant future visions")

Question: Please tell me which class the source paper 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)