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 "exploring the effectiveness of service decomposition in fog computing architecture for the internet of things"):
  Abstract: The Internet of Things (IoT) aims to connect everyday physical objects to the internet. These objects will produce a significant amount of data. The traditional cloud computing architecture aims to process data in the cloud. As a result, a significant amount of data needs to be communicated to the cloud. This creates a number of challenges, such as high communication latency between the devices and the cloud, increased energy consumption of devices during frequent data upload to the cloud, high bandwidth consumption, while making the network busy by sending the data continuously, and less privacy because of less control on the transmitted data to the server. Fog computing has been proposed to counter these weaknesses. Fog computing aims to process data at the edge and substantially eliminate the necessity of sending data to the cloud. However, combining the Service Oriented Architecture (SOA) with the fog computing architecture is still an open challenge. In this paper, we propose to decompose services to create linked-microservices (LMS). Linked-microservices are services that run on multiple nodes but closely linked to their linked-partners. Linked-microservices allow distributing the computation across different computing nodes in the IoT architecture. Using four different types of architectures namely cloud, fog, hybrid and fog+cloud, we explore and demonstrate the effectiveness of service decomposition by applying four experiments to three different type of datasets. Evaluation of the four architectures shows that decomposing services into nodes reduce the data consumption over the network by 10% - 70%. Overall, these results indicate that the importance of decomposing services in the context of fog computing for enhancing the quality of service.

Includes members:
  Member "fog computing for sustainable smart cities a survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "efficient 3d road map data exchange for intelligent vehicles in vehicular fog networks")
  Member "enorm a framework for edge node resource management":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "context aware distribution of fog applications using deep reinforcement learning")
  Member "microservices granularity vs performance":
    also connected through other hyperedges:
      (none)
  Member "privacy preserving personal model training":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "optimizing deep learning inference on embedded systems through adaptive model selection")
  Member "a hybrid approach for data analytics for internet of things":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a pub sub sdn integrated framework for iot traffic orchestration")
  Member "valorising the iot databox creating value for everyone":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "smart home personal assistants a security and privacy review")
  Member "a survey on internet of things from industrial market perspective":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a throughput and energy efficiency scheme for unlicensed massive machine type communications")

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.NI(Networking and Internet Architecture)