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 "machine learning based hybrid precoding for mmwave mimo ofdm with dynamic subarray"):
  Abstract: Hybrid precoding design can be challenging for broadband millimeter-wave (mmWave) massive MIMO due to the frequency-flat analog precoder in radio frequency (RF). Prior broadband hybrid precoding work usually focuses on fully-connected array (FCA), while seldom considers the energy-efficient partially-connected subarray (PCS) including the fixed subarray (FS) and dynamic subarray (DS). Against this background, this paper proposes a machine learning based broadband hybrid precoding for mmWave massive MIMO with DS. Specifically, we first propose an optimal hybrid precoder based on principal component analysis (PCA) for the FS, whereby the frequency-flat RF precoder for each subarray is extracted from the principle component of the optimal frequency-selective precoders for fully-digital MIMO. Moreover, we extend the PCA-based hybrid precoding to DS, where a shared agglomerative hierarchical clustering (AHC) algorithm developed from machine learning is proposed to group the DS for improved spectral efficiency (SE). Finally, we investigate the energy efficiency (EE) of the proposed scheme for both passive and active antennas. Simulations have confirmed that the proposed scheme outperforms conventional schemes in both SE and EE.

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 "compressive sensing techniques for next generation wireless communications":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dl based csi feedback and cooperative recovery in massive mimo")
  Member "codebook based hybrid precoding for millimeter wave multiuser systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hybrid precoder design for cache enabled millimeter wave radio access networks")
  Member "2d unitary esprit based super resolution channel estimation for millimeter wave massive mimo with hybrid precoding":
    also connected through other hyperedges:
      (none)
  Member "codebook design for millimeter wave channel estimation with hybrid precoding structure":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "channel estimation and hybrid precoding for distributed phased arrays based mimo wireless communications")
  Member "dynamic subarrays for hybrid precoding in wideband mmwave mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "gmd based hybrid beamforming for large reconfigurable intelligent surface assisted millimeter wave massive mimo")
  Member "frequency selective hybrid precoding for limited feedback millimeter wave systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gmd based hybrid beamforming for large reconfigurable intelligent surface assisted millimeter wave massive mimo")
  Member "hybrid mimo architectures for millimeter wave communications phase shifters or switches":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "task based quantization with application to mimo receivers")

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)