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 "managing fog networks using reinforcement learning based load balancing algorithm"):
  Abstract: The powerful paradigm of Fog computing is currently receiving major interest, as it provides the possibility to integrate virtualized servers into networks and brings cloud service closer to end devices. To support this distributed intelligent platform, Software-Defined Network (SDN) has emerged as a viable network technology in the Fog computing environment. However, uncertainties related to task demands and the different computing capacities of Fog nodes, inquire an effective load balancing algorithm. In this paper, the load balancing problem has been addressed under the constraint of achieving the minimum latency in Fog networks. To handle this problem, a reinforcement learning based decision-making process has been proposed to find the optimal offloading decision with unknown reward and transition functions. The proposed process allows Fog nodes to offload an optimal number of tasks among incoming tasks by selecting an available neighboring Fog node under their respective resource capabilities with the aim to minimize the processing time and the overall overloading probability. Compared with the traditional approaches, the proposed scheme not only simplifies the algorithmic framework without imposing any specific assumption on the network model but also guarantees convergence in polynomial time. The results show that, during average delays, the proposed reinforcement learning-based offloading method achieves significant performance improvements over the variation of service rate and traffic arrival rate. The proposed algorithm achieves 1.17%, 1.02%, and 3.21% lower overload probability relative to random, least-queue and nearest offloading selection schemes, respectively.

Includes members:
  Member "an online optimization framework for distributed fog network formation with minimal latency":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "asynchronous mobile edge computation offloading energy efficient resource management")
  Member "a view of fog computing from networking perspective":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "opportunistic edge computing concepts opportunities and research challenges")
  Member "fog computing based radio access networks issues and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "mode selection and resource allocation in sliced fog radio access networks a reinforcement learning approach")
  Member "what is a fog node a tutorial on current concepts towards a common definition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey of big data machine learning applications optimization in cloud data centers and networks")

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)