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: "ifogsim a toolkit for modeling and simulation of resource management techniques in internet of things edge and fog computing environments"
  Abstract: Internet of Things (IoT) aims to bring every object (e.g. smart cameras, wearable, environmental sensors, home appliances, and vehicles) online, hence generating massive amounts of data that can overwhelm storage systems and data analytics applications. Cloud computing offers services at the infrastructure level that can scale to IoT storage and processing requirements. However, there are applications such as health monitoring and emergency response that require low latency, and delay caused by transferring data to the cloud and then back to the application can seriously impact their performances. To overcome this limitation, Fog computing paradigm has been proposed, where cloud services are extended to the edge of the network to decrease the latency and network congestion. To realize the full potential of Fog and IoT paradigms for real-time analytics, several challenges need to be addressed. The first and most critical problem is designing resource management techniques that determine which modules of analytics applications are pushed to each edge device to minimize the latency and maximize the throughput. To this end, we need a evaluation platform that enables the quantification of performance of resource management policies on an IoT or Fog computing infrastructure in a repeatable manner. In this paper we propose a simulator, called iFogSim, to model IoT and Fog environments and measure the impact of resource management techniques in terms of latency, network congestion, energy consumption, and cost. We describe two case studies to demonstrate modeling of an IoT environment and comparison of resource management policies. Moreover, scalability of the simulation toolkit in terms of RAM consumption and execution time is verified under different circumstances.

Connected through hyperedge HE-1 (induced by source paper "the role of big data analytics in industrial internet of things"):
  with members:
    - "mobile cloud business process management system for the internet of things a survey"
    - "enabling smart data noise filtering in big data classification"
    - "graph of virtual actors gova a big data analytics architecture for iot"
    - "valorising the iot databox creating value for everyone"
    - "mllib machine learning in apache spark"
    - "the emerging internet of things marketplace from an industrial perspective a survey"
    - "a survey on internet of things from industrial market perspective"

Connected through hyperedge HE-2 (induced by source paper "a survey of fog computing and communication current researches and future directions"):
  with members:
    - "fog computing a taxonomy survey and future directions"
    - "a survey on mobile edge networks convergence of computing caching and communications"
    - "coding for distributed fog computing"
    - "the fog makes sense enabling social sensing services with limited internet connectivity"
    - "wireless caching technical misconceptions and business barriers"
    - "fog computing based radio access networks issues and challenges"
    - "the price of fog a data driven study on caching architectures in vehicular networks"
    - "fit a fog computing device for speech teletreatments"

Connected through hyperedge HE-3 (induced by source paper "fog computing a taxonomy survey and future directions"):
  with members:
    - "fog computing based radio access networks issues and challenges"
    - "user access mode selection in fog computing based radio access networks"
    - "fog computing principles architectures and applications"
    - "challenges and opportunities in edge computing"

Connected through hyperedge HE-4 (induced by source paper "emufog extensible and scalable emulation of large scale fog computing infrastructures"):
  with members:
    - "the fog makes sense enabling social sensing services with limited internet connectivity"

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)