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 "fog computing survey of trends architectures requirements and research directions"):
  Abstract: Emerging technologies like the Internet of Things (IoT) require latency-aware computation for real-time application processing. In IoT environments, connected things generate a huge amount of data, which are generally referred to as big data. Data generated from IoT devices are generally processed in a cloud infrastructure because of the on-demand services and scalability features of the cloud computing paradigm. However, processing IoT application requests on the cloud exclusively is not an efficient solution for some IoT applications, especially time-sensitive ones. To address this issue, Fog computing, which resides in between cloud and IoT devices, was proposed. In general, in the Fog computing environment, IoT devices are connected to Fog devices. These Fog devices are located in close proximity to users and are responsible for intermediate computation and storage. Fog computing research is still in its infancy, and taxonomy-based investigation into the requirements of Fog infrastructure, platform, and applications mapped to current research is still required. This paper starts with an overview of Fog computing in which the definition of Fog computing, research trends, and the technical differences between Fog and cloud are reviewed. Then, we investigate numerous proposed Fog computing architecture and describe the components of these architectures in detail. From this, the role of each component will be defined, which will help in the deployment of Fog computing. Next, a taxonomy of Fog computing is proposed by considering the requirements of the Fog computing paradigm. We also discuss existing research works and gaps in resource allocation and scheduling, fault tolerance, simulation tools, and Fog-based microservices. Finally, by addressing the limitations of current research works, we present some open issues, which will determine the future research direction.

Includes members:
  Member "fog computing a taxonomy survey and future directions":
    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 "fog assisted wiot a smart fog gateway for end to end analytics in wearable internet of things":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "healthfog an ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated iot and fog computing environments")
  Member "fog computing for sustainable smart cities a survey":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient 3d road map data exchange for intelligent vehicles in vehicular fog networks")
  Member "demystifying fog computing characterizing architectures applications and abstractions":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fog to cloud and network coded based architecture minimizing data download time for smart mobility")
  Member "fog computing principles architectures and applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "characterizing application scheduling on edge fog and cloud computing resources")
  Member "fog data enhancing telehealth big data through fog computing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on edge computing systems and tools")
  Member "big data computing and clouds":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "authentication access control privacy threats and trust management towards securing fog computing environments a review")

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)