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 "toward operator to waveform 5g radio access network slicing"):
  Abstract: Radio access network (RAN) slicing realizes a vision where physical network resources that belong to a specific infrastructure provider can be shared among multiple mobile network operators (MNOs). Existing work in this area has addressed RAN slicing at different levels of network abstractions, but has often neglected the multitude of tightly intertwined inter-level operations involved in real-world slicing systems. For this reason, this article discusses a novel framework for operator-to-waveform 5G RAN slicing. In the proposed framework, slicing operations are treated holistically, including MNO's selection of base stations (BSs) and maximum number of users, down to the waveform-level scheduling of resource blocks. Experimental results show that the proposed framework provides up to 150% improvement in terms of number of resource blocks that can be used to enable 5G transmission technologies that require coordination and synchronization among BSs.

Includes members:
  Member "big data goes small real time spectrum driven embedded wireless networking through deep learning in the rf loop":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deepradioid real time channel resilient optimization of deep learning based radio fingerprinting algorithms")
  Member "low complexity distributed radio access network slicing algorithms and experimental results":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "constrained network slicing games achieving service guarantees and network efficiency")
  Member "securing the internet of things in the age of machine learning and software defined networking":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "security privacy in iot using machine learning blockchain threats countermeasures")
  Member "wnos an optimization based wireless network operating system":
    also connected through other hyperedges:
      (none)
  Member "network slicing for 5g with sdn nfv concepts architectures and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges")
  Member "network slicing to enable scalability and flexibility in 5g mobile networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges")
  Member "a survey on low latency towards 5g ran core network and caching solutions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "the design of dynamic probabilistic caching with time varying content popularity")
  Member "survey on network virtualization hypervisors for software defined networking":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "5g network slicing using sdn and nfv a survey of taxonomy architectures and future challenges")

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)