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 "r fft function split at ifft fft in unified lte cran and cable access network"):
  Abstract: The remote-PHY (R-PHY) modular cable network for data over cable service interface specification (DOCSIS) service conducts the physical layer processing for the transmissions over the broadcast cable in a remote node. In contrast, the cloud radio access network (CRAN) for long-term evolution (LTE) cellular wireless services conducts all baseband physical layer processing in a central baseband unit and the remaining physical layer processing steps toward radio frequency (RF) transmission in remote nodes. Both DOCSIS and LTE are based on orthogonal frequency division multiplexing physical layer processing. We propose to unify cable and wireless cellular access networks by utilizing the hybrid fiber-coax (HFC) cable network infrastructure as fiber fronthaul network for cellular wireless services. For efficient operation of such a unified access network, we propose a novel remote-fast-Fourier transform (R-FFT) node that conducts the physical layer processing from the FFT module toward the RF transmission, whereby DOCSIS and LTE share a common FFT module. The frequency domain in-phase and quadrature (I/Q) symbols for both DOCSIS and LTE are transmitted over the fiber between remote node and cable headend, where the remaining physical layer processing is conducted. We further propose to cache repetitive quadrature amplitude modulation (QAM) symbols in the R-FFT node to reduce the fronthaul bitrate requirements and enable statistical multiplexing. We evaluate the fronthaul bitrate reductions achieved by R-FFT node caching, the fronthaul transmission bitrates arising from the unified DOCSIS and LTE service, and illustrate the delay implications of moving part of the cable R-PHY remote node physical layer processing to the headend. Overall, our evaluations indicate that the proposed R-FFT node can effectively support unified DOCSIS and LTE services over the HFC cable plant while substantially reducing the fronthaul bitrate requirements of the existing CRAN structures.

Includes members:
  Member "on the fronthaul statistical multiplexing gain":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "flexible functional split and power control for energy harvesting cloud radio access networks")
  Member "recent advances in cloud radio access networks system architectures key techniques and open issues":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multiple antenna technologies for beyond 5g")
  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-3 (induced by source paper "data correlation aware resource management in wireless virtual reality vr an echo state transfer learning approach")
  Member "statistical multiplexing gain analysis of heterogeneous virtual base station pools in cloud radio access networks":
    also connected through other hyperedges:
      (none)
  Member "software defined optical networks sdons a comprehensive survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey of big data machine learning applications optimization in cloud data centers and networks")
  Member "upstream polling protocols for flow control in pon xdsl hybrid access networks":
    also connected through other hyperedges:
      (none)
  Member "green ofdma resource allocation in cache enabled cran":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "cooperative multi bitrate video caching and transcoding in multicarrier noma assisted heterogeneous virtualized mec networks")
  Member "optimized configurable architectures for scalable soft input soft output mimo detectors with 256 qam":
    also connected through other hyperedges:
      (none)

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)