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: "distributed optimization for energy efficient fog computing in the tactile internet"
  Abstract: Tactile Internet is an emerging concept that focuses on supporting high-fidelity, ultra-responsive, and widely available human-to-machine interactions. To reduce the transmission latency and alleviate Internet congestion, fog computing has been advocated as an important component of the Tactile Internet. In this paper, we focus on an energy-efficient design of fog computing networks that support low-latency Tactile Internet applications. We investigate two performance metrics: Service response time of end-users and power usage efficiency of fog nodes. We quantify the fundamental tradeoff between these two metrics and then extend our analysis to fog computing networks involving cooperation between fog nodes. We introduce a novel cooperative fog computing concept, referred to as offload forwarding, in which a set of fog nodes with different computing and energy resources can cooperate with each other. The objective of this cooperation is to balance the workload processed by different fog nodes, further reduce the service response time, and improve the efficiency of power usage. We develop a distributed optimization framework based on dual decomposition to achieve the optimal tradeoff. Our framework does not require fog nodes to disclose their private information nor conduct back-and-forth negotiations with each other. Two distributed optimization algorithms are proposed. One is based on the subgradient method with dual decomposition and the other is based on distributed alternating direction method of multipliers via variable splitting. We prove that both algorithms can achieve the optimal workload allocation that minimizes the response time under the given power efficiency constraints of fog nodes. Finally, to evaluate the performance of our proposed concept, we simulate a possible implementation of a city-wide self-driving bus system supported by fog computing in the city of Dublin. The fog computing network topology is set based on a real cellular network infrastructure involving 200 base stations deployed by a major cellular operator in Ireland. Numerical results show that our proposed framework can balance the power usage efficiency among fog nodes and reduce the service latency for users by around 50% in urban scenarios.

Connected through hyperedge HE-1 (induced by source paper "towards tactile internet in beyond 5g era recent advances current issues and future directions"):
  with members:
    - "emerging edge computing technologies for distributed iot systems"
    - "the effect of iot new features on security and privacy new threats existing solutions and challenges yet to be solved"
    - "ultrareliable and low latency communication techniques for tactile internet services"
    - "scalable intelligence enabled networking with traffic engineering in 5g scenarios for future audio visual tactile internet"
    - "joint uplink and downlink resource configuration for ultra reliable and low latency communications"
    - "urllc embb slicing to support vr multimodal perceptions over wireless cellular systems"
    - "robust multi path communications for uavs in the urban iot"
    - "a d2d based protocol for ultra reliable wireless communications for industrial automation"

Connected through hyperedge HE-2 (induced by source paper "joint power control and rate allocation enabling ultra reliability and energy efficiency in simo wireless networks"):
  with members:
    - "distributed rate control in downlink noma networks with reliability constraints"
    - "rate control under finite blocklength for downlink cellular networks with reliability constraints"
    - "energy latency tradeoff in ultra reliable low latency communication with retransmissions"
    - "joint uplink and downlink resource configuration for ultra reliable and low latency communications"
    - "ultra reliable and low latency communications in 5g downlink physical layer aspects"
    - "ultra reliable cooperative short packet communications with wireless energy transfer"
    - "on the effective energy efficiency of ultra reliable networks in the finite blocklength regime"
    - "realizing the tactile internet haptic communications over next generation 5g cellular networks"

Connected through hyperedge HE-3 (induced by source paper "dynamic network slicing for scalable fog computing systems with energy harvesting"):
  with members:
    - "computing resource allocation in three tier iot fog networks a joint optimization approach combining stackelberg game and matching"
    - "sdfog a software defined computing architecture for qos aware service orchestration over edge devices"
    - "response time optimized distributed cloud resource allocation"
    - "fog computing principles architectures and applications"
    - "from network sharing to multi tenancy the 5g network slice broker"
    - "network slicing games enabling customization in multi tenant networks"
    - "spatial spectrum and energy efficiency of random cellular networks"
    - "energy harvesting wireless communications a review of recent advances"

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)