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 radio access networks for various iot environments"):
  Abstract: Fog radio access network (F-RAN) has been recently proposed to satisfy the low-latency communication requirements of Internet of Things (IoT) applications. We consider the problem of sequentially allocating the limited resources of a fog node to a heterogeneous population of IoT applications with varying latency requirements. Specifically, for each service request it receives in time, fog node needs to decide whether to serve that user locally to provide it with low-latency communication service or to refer it to the cloud control center to keep valuable fog resources available for future users with potentially higher utility to the system (i.e., lower latency requirement). We formulate the problem as a Markov Decision Process (MDP) in two alternative formulations: infinite-horizon MDP (IH MDP) and finite-horizon MDP (FH MDP). In both IH and FH formulations, we present the optimal solution, known as the optimal policy, through Reinforcement Learning (RL). The optimal policies in both cases are learnt from the IoT environment using different RL methods. The significant advantage of the proposed RL methods over the straightforward approach of deciding based on a fixed threshold of utility is that the RL methods quickly learn the optimal decision thresholds from the IoT environment, and thus always achieve the best possible performance regardless of the environment. They strike the right balance between the two conflicting objectives, maximize the average total served utility vs. minimize the fog node's idle time. Extensive simulation results for various IoT environments corroborate the theoretical underpinnings of the proposed RL methods.

Includes members:
  Member "a survey on mobile edge networks convergence of computing caching and communications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (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-2 (induced by source paper "how much can d2d communication reduce content delivery latency in fog networks with edge caching")
  Member "learning how to communicate in the internet of things finite resources and heterogeneity":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on machine learning based performance improvement of wireless networks phy mac and network layer")
  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-4 (induced by source paper "data correlation aware resource management in wireless virtual reality vr an echo state transfer learning approach")
  Member "fronthaul constrained cloud radio access networks insights and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "on the capacity regions of cloud radio access networks with limited orthogonal fronthaul")
  Member "delay aware uplink fronthaul allocation in cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reinforcement learning based resource allocation in fog ran for iot with heterogeneous latency requirements")

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)