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 "reinforcement learning based resource allocation in fog ran for iot with heterogeneous latency requirements"):
  Abstract: In light of the quick proliferation of Internet of things (IoT) devices and applications, fog radio access network (Fog-RAN) has been recently proposed for fifth generation (5G) wireless communications to assure the requirements of ultra-reliable low-latency communication (URLLC) for the IoT applications which cannot accommodate large delays. Hence, fog nodes (FNs) are equipped with computing, signal processing and storage capabilities to extend the inherent operations and services of the cloud to the edge. We consider the problem of sequentially allocating the FN's limited resources to the IoT applications of heterogeneous latency requirements. For each access request from an IoT user, the FN needs to decide whether to serve it locally utilizing its own resources or to refer it to the cloud to conserve its valuable resources for future users of potentially higher utility to the system (i.e., lower latency requirement). We formulate the Fog-RAN resource allocation problem in the form of a Markov decision process (MDP), and employ several reinforcement learning (RL) methods, namely Q-learning, SARSA, Expected SARSA, and Monte Carlo, for solving the MDP problem by learning the optimum decision-making policies. We verify the performance and adaptivity of the RL methods and compare it with the performance of a fixed-threshold-based algorithm. Extensive simulation results considering 19 IoT environments of heterogeneous latency requirements corroborate that RL methods always achieve the best possible performance regardless of the IoT environment.

Includes members:
  Member "optimizing the number of fog nodes for cloud fog thing networks":
    also connected through other hyperedges:
      (none)
  Member "resource allocation in noma based fog radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fog aided device to device networks with opportunistic content delivery")
  Member "a survey on mobile edge networks convergence of computing caching and communications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fog computing systems state of the art research issues and future trends")
  Member "recent advances in fog radio access networks performance analysis and radio resource allocation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "how much can d2d communication reduce content delivery latency in fog networks with edge caching")
  Member "recent advances in cloud radio access networks system architectures key techniques and open issues":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multiple antenna technologies for beyond 5g")
  Member "learning how to communicate in the internet of things finite resources and heterogeneity":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  Member "joint optimization of cloud and edge processing for fog radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "power efficient resource allocation in c rans with sinr constraints and deadlines")
  Member "cluster content caching an energy efficient approach to improve quality of service in cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "data correlation aware resource management in wireless virtual reality vr an echo state transfer learning approach")

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.NI(Networking and Internet Architecture)