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: "on reducing iot service delay via fog offloading"
  Abstract: With the Internet of Things (IoT) becoming a major component of our daily life, understanding how to improve the quality of service for IoT applications through fog computing is becoming an important problem. In this paper, we introduce a general framework for IoT-fog-cloud applications, and propose a delay-minimizing collaboration and offloading policy for fog-capable devices that aims to reduce the service delay for IoT applications. We then develop an analytical model to evaluate our policy and show how the proposed framework helps to reduce IoT service delay.

Connected through hyperedge HE-1 (induced by source paper "modelling fog offloading performance"):
  with members:
    - "performance estimation of container based cloud to fog offloading"
    - "defog fog computing benchmarks"
    - "resource management in fog edge computing a survey"
    - "live service migration in mobile edge clouds"
    - "enorm a framework for edge node resource management"
    - "challenges and opportunities in edge computing"

Connected through hyperedge HE-2 (induced by source paper "all one needs to know about fog computing and related edge computing paradigms a complete survey"):
  with members:
    - "pcamp performance comparison of machine learning packages on the edges"
    - "are fpgas suitable for edge computing"
    - "cabaret leveraging recommendation systems for mobile edge caching"
    - "edge intelligence on demand deep learning model co inference with device edge synergy"
    - "fog computing a taxonomy survey and future directions"
    - "data analytics service composition and deployment on edge devices"
    - "live service migration in mobile edge clouds"
    - "towards mission critical control at the edge and over 5g"

Connected through hyperedge HE-3 (induced by source paper "process migration based computational offloading framework for iot supported mobile edge cloud computing"):
  with members:
    - "edge cloud offloading algorithms issues methods and perspectives"
    - "a survey on mobile edge computing the communication perspective"
    - "hierarchical fog cloud computing for iot systems a computation offloading game"
    - "dmtcp transparent checkpointing for cluster computations and the desktop"

Connected through hyperedge HE-4 (induced by source paper "application component placement in nfv based hybrid cloud fog systems with mobile fog nodes"):
  with members:
    - "application component placement in nfv based hybrid cloud fog systems"
    - "a comprehensive survey on fog computing state of the art and research challenges"
    - "network functions virtualization architecture for gateways for virtualized wireless sensor and actuator networks"
    - "network function virtualization state of the art and research challenges"

Connected through hyperedge HE-5 (induced by source paper "performance estimation of container based cloud to fog offloading"):
  with members:
    - "resource management in fog edge computing a survey"
    - "enorm a framework for edge node resource management"

Connected through hyperedge HE-6 (induced by source paper "deep reinforcement learning based mode selection and resource management for green fog radio access networks"):
  with members:
    - "learning to optimize training deep neural networks for interference management"
    - "layered group sparse beamforming for cache enabled green wireless networks"
    - "an evolutionary game for user access mode selection in fog radio access networks"
    - "deep reinforcement learning an overview"
    - "a deep reinforcement learning based framework for content caching"
    - "energy efficient resource allocation optimization for multimedia heterogeneous cloud radio access networks"
    - "fog computing based radio access networks issues and challenges"
    - "csi based fingerprinting for indoor localization a deep learning approach"

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.NI(Networking and Internet Architecture)