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 "hybrid analog digital channel estimation and beamforming training throughput tradeoff"):
  Abstract: This paper develops hybrid analog-digital channel estimation and beamforming techniques for multiuser massive multiple-input multiple-output (MIMO) systems with limited number of radio frequency (RF) chains. For these systems, first, we design novel minimum-mean-squared error (MMSE) hybrid analog-digital channel estimator by considering both cases with perfect and imperfect channel covariance matrix knowledge. Then, we utilize the estimated channels to enable beamforming for data transmission. When the channel covariance matrices of all user equipments (UEs) are known perfectly, we show that there is a tradeoff between the training duration and throughput. Specifically, we exploit the fact that the optimal training duration that maximizes the throughput depends on the covariance matrices of all UEs, number of RF chains, and channel coherence time (  $T_c$  ). We also show that the training time optimization problem can be formulated as a concave maximization problem where its global optimal solution can be obtained efficiently using existing tools. The analytical expressions are validated by performing extensive Monte Carlo simulations.

Includes members:
  Member "on the number of rf chains and phase shifters and scheduling design with hybrid analog digital beamforming":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hybrid transceiver optimization for multi hop communications")
  Member "low rank spatial channel estimation for millimeter wave cellular systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback")
  Member "channel estimation and hybrid precoding for millimeter wave cellular systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback")
  Member "millimeter wave cellular wireless networks potentials and challenges":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "wirelessly powered sensor networks power allocation for channel estimation and energy beamforming")
  Member "spatially sparse precoding in millimeter wave mimo 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 "low complexity polynomial channel estimation in large scale mimo with arbitrary statistics":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "channel estimation for spatially temporally correlated massive mimo systems with one bit adcs")
  Member "low complexity hybrid precoding in massive multiuser mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "energy efficient hybrid precoding design for integrated multicast unicast millimeter wave communications with swipt")
  Member "millimeter wave channel modeling and cellular capacity evaluation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "coverage analysis of uavs in millimeter wave networks a stochastic geometry 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)