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 "boosting fronthaul capacity global optimization of power sharing for centralized radio access network"):
  Abstract: The limited fronthaul capacity imposes a challenge on the uplink of centralized radio access network (C-RAN). We propose to boost the fronthaul capacity of massive multiple-input multiple-output (MIMO) aided C-RAN by globally optimizing the power sharing between channel estimation and data transmission both for the user devices (UDs) and the remote radio units (RRUs). Intuitively, allocating more power to the channel estimation will result in more accurate channel estimates, which increases the achievable throughput. However, increasing the power allocated to the pilot training will reduce the power assigned to data transmission, which reduces the achievable throughput. In order to optimize the powers allocated to the pilot training and to the data transmission of both the UDs and the RRUs, we assign an individual power sharing factor to each of them and derive an asymptotic closed-form expression of the signal-to-interference-plus-noise for the massive MIMO aided C-RAN consisting of both the UD-to-RRU links and the RRU-to-baseband unit (BBU) links. We then exploit the C-RAN architecture's central computing and control capability for jointly optimizing the UDs' power sharing factors and the RRUs' power sharing factors aiming for maximizing the fronthaul capacity. Our simulation results show that the fronthaul capacity is significantly boosted by the proposed global optimization of the power allocation between channel estimation and data transmission both for the UDs and for their host RRUs. As a specific example of 32 receive antennas (RAs) deployed by RRU and 128 RAs deployed by BBU, the sum-rate of 10 UDs achieved with the optimal power sharing factors improves 33\% compared with the one attained without optimizing power sharing factors.

Includes members:
  Member "differential evolution algorithm aided turbo channel estimation and multi user detection for g fast systems in the presence of fext":
    also connected through other hyperedges:
      (none)
  Member "regularized zero forcing precoding aided adaptive coding and modulation for large scale antenna array based air to air communications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "aeronautical ad hoc networking for the internet above the clouds")
  Member "joint precoding and rrh selection for user centric green mimo c ran":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "hybrid precoder design for cache enabled millimeter wave radio access networks")
  Member "joint user selection and energy minimization for ultra dense multi channel c ran with incomplete csi":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "robust beamforming with pilot reuse scheduling in a heterogeneous cloud radio access network")
  Member "overview of millimeter wave communications for fifth generation 5g wireless networks with a focus on propagation models":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "suburban fixed wireless access channel measurements and models at 28 ghz for 90 outdoor coverage")
  Member "adaptive coding and modulation for large scale antenna array based aeronautical communications in the presence of co channel interference":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secure millimeter wave cloud radio access networks relying on microwave multicast fronthaul")
  Member "multivariate fronthaul quantization for downlink c ran":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi tenant c ran with spectrum pooling downlink optimization under privacy constraints")
  Member "fronthauling for 5g lte u ultra dense cloud small cell networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "joint fronthaul multicast and cooperative beamforming for cache enabled cloud based small cell networks an mds codes aided approach")

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.IT(Information Theory)