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 "leveraging synergy of 5g sdwn and multi layer resource management for network optimization"):
  Abstract: Fifth-generation (5G) cellular wireless networks are envisioned to predispose service-oriented, flexible, and spectrum/energy-efficient edge-to-core infrastructure, aiming to offer diverse applications. Convergence of software-defined networking (SDN), software-defined radio (SDR) compatible with multiple radio access technologies (RATs), and virtualization on the concept of 5G software-defined wireless networking (5G-SDWN) is a promising approach to provide such a dynamic network. The principal technique behind the 5G-SDWN framework is the separation of the control and data planes, from the deep core entities to edge wireless access points (APs). This separation allows the abstraction of resources as transmission parameters of each user over the 5G-SDWN. In this user-centric and service-oriented environment, resource management plays a critical role to achieve efficiency and reliability. However, it is natural to wonder if 5G-SDWN can be leveraged to enable converged multi-layer resource management over the portfolio of resources, and reciprocally, if CML resource management can effectively provide performance enhancement and reliability for 5G-SDWN. We believe that replying to these questions and investigating this mutual synergy are not trivial, but multidimensional and complex for 5G-SDWN, which consists of different technologies and also inherits legacy generations of wireless networks. In this paper, we propose a flexible protocol structure based on three mentioned pillars for 5G-SDWN, which can handle all the required functionalities in a more crosslayer manner. Based on this, we demonstrate how the general framework of CML resource management can control the end user quality of experience. For two scenarios of 5G-SDWN, we investigate the effects of joint user-association and resource allocation via CML resource management to improve performance in a virtualized network.

Includes members:
  Member "virtualization of multi cell 802 11 networks association and airtime control":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "joint user association and resource allocation in virtualized wireless networks")
  Member "performance analysis of massive mimo for cell boundary users":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a retrodirective wireless power transfer scheme for ambient backscatter systems")
  Member "joint user association and resource allocation in virtualized wireless networks":
    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 "5g cellular key enabling technologies and research challenges":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "distributed artificial intelligence solution for d2d communication in 5g networks")
  Member "system architecture and key technologies for 5g heterogeneous cloud radio access networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "integer forcing architectures for uplink cloud radio access networks")
  Member "optimal user association for massive mimo empowered ultra dense wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "load optimization with user association in cooperative and load coupled lte networks")
  Member "optimal user cell association for massive mimo wireless networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reinforcement learning based vehicle cell association algorithm for highly mobile millimeter wave communication")

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)