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 "an online optimization framework for distributed fog network formation with minimal latency"):
  Abstract: Fog computing is emerging as a promising paradigm to perform distributed, low-latency computation by jointly exploiting the radio and computing resources of end-user devices and cloud servers. However, the dynamic and distributed formation of local fog networks is highly challenging due to the unpredictable arrival and departure of neighboring fog nodes. Therefore, a given fog node must properly select a set of neighboring nodes and intelligently offload its computational tasks to this set of neighboring fog nodes and the cloud in order to achieve low-latency transmission and computation. In this paper, the problem of fog network formation and task distribution is jointly investigated while considering a hybrid fog-cloud architecture. The goal is to minimize the maximum computational latency by enabling a given fog node to form a suitable fog network and optimize the task distribution, under uncertainty on the arrival process of neighboring fog nodes. To solve this problem, a novel online optimization framework is proposed in which the neighboring nodes are selected by using a threshold-based online algorithm that uses a target competitive ratio, defined as the ratio between the latency of the online algorithm and the offline optimal latency. The proposed framework repeatedly updates its target competitive ratio and optimizes the distribution of the fog node's computational tasks in order to minimize latency. Simulation results show that the proposed framework can successfully select a set of neighboring nodes while reducing latency by up to 19.25% compared to a baseline approach based on the well-known online secretary framework. The results also show how, using the proposed framework, the computational tasks can be properly offloaded between the fog network and a remote cloud server in different network settings.

Includes members:
  Member "proactive edge computing in latency constrained fog networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "wireless edge computing with latency and reliability guarantees")
  Member "an online secretary framework for fog network formation with minimal latency":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "all one needs to know about fog computing and related edge computing paradigms a complete survey")
  Member "enorm a framework for edge node resource management":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "context aware distribution of fog applications using deep reinforcement learning")
  Member "joint subcarrier and cpu time allocation for mobile edge computing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "client selection for federated learning with heterogeneous resources in mobile edge")
  Member "fog computing based radio access networks issues and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mode selection and resource allocation in sliced fog radio access networks a reinforcement learning approach")
  Member "learning how to communicate in the internet of things finite resources and heterogeneity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "challenges and opportunities in edge computing":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "context aware distribution of fog applications using deep reinforcement learning")
  Member "unmanned aerial vehicle with underlaid device to device communications performance and tradeoffs":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "performance analysis of mobile cellular connected drones under practical antenna configurations")

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.IT(Information Theory)