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 beamforming for millimeter wave systems using the mmse criterion"):
  Abstract: Hybrid analog and digital beamforming (HBF) has recently emerged as an attractive technique for millimeter-wave (mmWave) communication systems. It well balances the demand for sufficient beamforming gains to overcome the propagation loss and the desire to reduce the hardware cost and power consumption. In this paper, the mean square error (MSE) is chosen as the performance metric to characterize the transmission reliability. Using the minimum sum-MSE criterion, we investigate the HBF design for broadband mmWave transmissions. To overcome the difficulty of solving the multi-variable design problem, the alternating minimization method is adopted to optimize the hybrid transmit and receive beamformers alternatively. Specifically, a manifold optimization-based HBF algorithm is first proposed, which directly handles the constant modulus constraint of the analog component. Its convergence is then proved. To reduce the computational complexity, we then propose a low-complexity general eigenvalue decomposition-based HBF algorithm in the narrowband scenario and three algorithms via the eigenvalue decomposition and orthogonal matching pursuit methods in the broadband scenario. A particular innovation in our proposed alternating minimization algorithms is a carefully designed initialization method, which leads to a faster convergence. Furthermore, we extend the sum-MSE-based design to that with weighted sum-MSE, which is then connected to the spectral efficiency-based design. Simulation results show that the proposed HBF algorithms achieve a significant performance improvement over existing ones and perform close to full-digital beamforming.

Includes members:
  Member "a hardware efficient analog network structure for hybrid precoding in millimeter wave systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "secure communication for spatially sparse millimeter wave massive mimo channels via hybrid precoding")
  Member "hybrid analog and digital beamforming for mmwave ofdm large scale antenna arrays":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a framework on hybrid mimo transceiver design based on matrix monotonic optimization")
  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 "on the number of rf chains and phase shifters and scheduling design with hybrid analog digital beamforming":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "hybrid transceiver optimization for multi hop communications")
  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 "frequency selective hybrid precoding for limited feedback millimeter wave systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gmd based hybrid beamforming for large reconfigurable intelligent surface assisted millimeter wave massive mimo")
  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.IT(Information Theory)