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 "on improving energy efficiency within green femtocell networks a hierarchical reinforcement learning approach"):
  Abstract: One of the efficient solutions of improving coverage and increasing capacity in cellular networks is the deployment of femtocells. As the cellular networks are becoming more complex, energy consumption of whole network infrastructure is becoming important in terms of both operational costs and environmental impacts. This paper investigates energy efficiency of two-tier femtocell networks through combining game theory and stochastic learning. With the Stackelberg game formulation, a hierarchical reinforcement learning framework is applied for studying the joint expected utility maximization of macrocells and femtocells subject to the minimum signal-to-interference-plus-noise-ratio requirements. In the learning procedure, the macrocells act as leaders and the femtocells are followers. At each time step, the leaders commit to dynamic strategies based on the best responses of the followers, while the followers compete against each other with no further information but the leaders' transmission parameters. In this paper, we propose two reinforcement learning based intelligent algorithms to schedule each cell's stochastic power levels. Numerical experiments are presented to validate the investigations. The results show that the two learning algorithms substantially improve the energy efficiency of the femtocell networks.

Includes members:
  Member "dynamics of boltzmann q learning in two player two action games":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "replicator dynamics with turnover of players")
  Member "price based resource allocation for spectrum sharing femtocell networks a stackelberg game approach":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "joint dynamic pricing and radio resource allocation framework for iot services")
  Member "power control in two tier femtocell networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "game theory based joint task offloading and resources allocation algorithm for mobile edge computing")
  Member "interference mitigation using uplink power control for two tier femtocell networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient interference management policies for femtocell networks")
  Member "introducing hierarchy in energy games":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "distributed power control with partial channel state information performance characterization and design")
  Member "femtocell networks a survey":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "robust resource allocation for pd noma based miso heterogeneous networks with comp technology")

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.LG(Machine Learning)