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 "joint optimization of computation and communication power in multi user massive mimo systems"):
  Abstract: With the growing interest in the deployment of massive multiple-input-multiple-output (MIMO) systems and millimeter wave technology for fifth generation (5G) wireless systems, the computation power to the total power consumption ratio is expected to increase rapidly due to high data traffic processing at the baseband unit. Therefore in this paper, a joint optimization problem of computation and communication power is formulated for multi-user massive MIMO systems with partially-connected structures of radio frequency (RF) transmission systems. When the computation power is considered for massiv MIMO systems, the results of this paper reveal that the energy efficiency of massive MIMO systems decreases with increasing the number of antennas and RF chains, which is contrary with the conventional energy efficiency analysis results of massive MIMO systems, i.e., only communication power is considered. To optimize the energy efficiency of multi-user massive MIMO systems, an upper bound on energy efficiency is derived. Considering the constraints on partially-connected structures, a suboptimal solution consisting of baseband and RF precoding matrices is proposed to approach the upper bound on energy efficiency of multi-user massive MIMO systems. Furthermore, an oPtimized Hybrid precOding with computation and commuNication powEr (PHONE) algorithm is developed to realize the joint optimization of computation and communication power. Simulation results indicate that the proposed algorithm improves energy and cost efficiencies and the maximum power saving is achieved by 76.59\% for multi-user massive MIMO systems with partially-connected structures.

Includes members:
  Member "energy efficient transceiver design for hybrid sub array architecture mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "energy efficient hybrid precoding design for integrated multicast unicast millimeter wave communications with swipt")
  Member "energy efficiency challenges of 5g small cell networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "neural networks in hybrid precoding for millimeter wave massive mimo systems")
  Member "alternating minimization algorithms for hybrid precoding in millimeter wave mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "beamforming design for large scale antenna arrays using deep learning")
  Member "an overview of signal processing techniques for millimeter wave mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "energy efficient hybrid precoding design for integrated multicast unicast millimeter wave communications with swipt")
  Member "energy efficiency optimization of 5g radio frequency chain systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "energy efficient hybrid precoding design for integrated multicast unicast millimeter wave communications with swipt")
  Member "hybrid digital and analog beamforming design for large scale antenna arrays":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "beamforming design for large scale antenna arrays using deep learning")
  Member "hybrid block diagonalization for massive multiuser mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "principal component analysis based broadband hybrid precoding for millimeter wave massive mimo systems")
  Member "channel estimation and hybrid precoding for millimeter wave cellular systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback")

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)