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 "dynamic model for network selection in next generation hetnets with memory affecting rational users"):
  Abstract: Recently, due to the staggering growth of wireless data traffic, heterogeneous networks have drawn tremendous attention due to the capabilities of enhancing the capacity/coverage and to save energy consumption for the next generation wireless networks. In this paper, we study a long-run user-centric network selection problem in the 5G heterogeneous network, where the network selection strategies of the users can be investigated dynamically. Unlike the conventional studies on the long-run model, we incorporate the memory effect and consider the fact that the decision-making of the users is affected by their memory, i.e., their past service experience. Namely, the users select the network based on not only their instantaneous achievable service experience but also their past service experience within their memory. Specifically, we model and study the interaction among the users in the framework of fractional evolutionary game based on the classical evolutionary game theory and the concept of the power-law memory. We analytically prove that the equilibrium of the fractional evolutionary game exists, is unique and uniformly stable. We also numerically demonstrate the stability of the fractional evolutionary equilibrium. Extensive numerical results have been conducted to evaluate the performance of the fractional evolutionary game. The numerical results have revealed some insightful findings. For example, the user in the fractional evolutionary game with positive memory effect can achieve a higher cumulative utility compared with the user in the fractional evolutionary game with negative memory effect. Moreover, the fractional evolutionary game with positive memory effect can reduce the loss in the user's cumulative utility caused by the small-scale fading.

Includes members:
  Member "a learning based coexistence mechanism for laa lte based hetnets":
    also connected through other hyperedges:
      (none)
  Member "energy minimization for wireless communication with rotary wing uav":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "joint trajectory and resource allocation design for energy efficient secure uav communication systems")
  Member "blocking probability and spatial throughput characterization for cellular enabled uav network with directional antenna":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "how do non ideal uav antennas affect air to ground communications")
  Member "downlink and uplink cell association with traditional macrocells and millimeter wave small cells":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "intelligent uav base station selection in urban environments a supervised learning approach")
  Member "green 5g heterogeneous networks through dynamic small cell operation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "energy efficient massive mimo through distributed precoder design")
  Member "physical layer security in millimeter wave cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secrecy outage analysis of non orthogonal spectrum sharing for heterogeneous cellular networks")
  Member "5g ultra dense cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "reinforcement learning based cooperative coded caching under dynamic popularities in ultra dense networks")
  Member "tractable model for rate in self backhauled millimeter wave cellular networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "joint path selection and rate allocation framework for 5g self backhauled mm wave 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.NI(Networking and Internet Architecture)