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: "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.

Connected through hyperedge HE-1 (induced by source paper "authentication access control privacy threats and trust management towards securing fog computing environments a review"):
  with members:
    - "user s privacy in recommendation systems applying online social network data a survey and taxonomy"
    - "mobile edge computing fog et al a survey and analysis of security threats and challenges"
    - "fogbus a blockchain based lightweight framework for edge and fog computing"
    - "blockchain in internet of things challenges and solutions"
    - "big data computing and clouds"
    - "combining ensemble methods and social network metrics for improving accuracy of ocsvm on intrusion detection in scada systems"
    - "non stationary resource allocation policies for delay constrained video streaming application to video over internet of things enabled networks"

Connected through hyperedge HE-2 (induced by source paper "a scalable iot fog framework for urban sound sensing"):
  with members:
    - "using cloud and fog computing for large scale iot based urban sound classification"
    - "deep learning for iot big data and streaming analytics a survey"
    - "deep convolutional neural networks and data augmentation for environmental sound classification"
    - "hierarchical fog cloud computing for iot systems a computation offloading game"

Connected through hyperedge HE-3 (induced by source paper "machine learning for wireless communications in the internet of things a comprehensive survey"):
  with members:
    - "helper heterogeneous efficient low power radio for enabling ad hoc emergency public safety networks"
    - "intelligent power control for spectrum sharing in cognitive radios a deep reinforcement learning approach"
    - "over the air deep learning based radio signal classification"
    - "a non parametric multi stage learning framework for cognitive spectrum access in iot networks"
    - "qarc video quality aware rate control for real time video streaming based on deep reinforcement learning"
    - "cognitive non orthogonal multiple access with cooperative relaying a new wireless frontier for 5g spectrum sharing"
    - "a survey of machine and deep learning methods for internet of things iot security"
    - "an introduction to deep learning for the physical layer"

Connected through hyperedge HE-4 (induced by source paper "fog computing applications taxonomy and requirements"):
  with members:
    - "all one needs to know about fog computing and related edge computing paradigms a complete survey"
    - "fog computing a taxonomy survey and future directions"
    - "a survey of state management in big data processing systems"
    - "foglearn leveraging fog based machine learning for smart system big data analytics"
    - "understanding performance of edge content caching for mobile video streaming"
    - "sdfog a software defined computing architecture for qos aware service orchestration over edge devices"
    - "decentralized computation offloading game for mobile cloud computing"
    - "long range communications in unlicensed bands the rising stars in the iot and smart city scenarios"

Connected through hyperedge HE-5 (induced by source paper "image classification on iot edge devices profiling and modeling"):
  with members:
    - "profiling and improving the duty cycling performance of linux based iot devices"
    - "empiot an energy measurement platform for wireless iot devices"
    - "applications of deep learning and reinforcement learning to biological data"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-6 (induced by source paper "fog computing architectures a reference for practitioners"):
  with members:
    - "fog computing principles architectures and applications"

Connected through hyperedge HE-7 (induced by source paper "multi criteria based dynamic user behaviour aware resource allocation in fog computing"):
  with members:
    - "fog computing architecture survey and challenges"

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)