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 "channel feedback based on aod adaptive subspace codebook in fdd massive mimo systems"):
  Abstract: Channel feedback is essential in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. Unfortunately, prior work on multiuser MIMO has shown that the feedback overhead scales linearly with the number of base station (BS) antennas, which is large in massive MIMO systems. To reduce the feedback overhead, we propose an angle-of-departure (AoD) adaptive subspace codebook for channel feedback in FDD massive MIMO systems. Our key insight is to leverage the observation that path AoDs vary more slowly than the path gains. Within the angle coherence time, by utilizing the constant AoD information, the proposed AoD-adaptive subspace codebook is able to quantize the channel vector in a more accurate way. From the performance analysis, we show that the feedback overhead of the proposed codebook only scales linearly with a small number of dominant (path) AoDs instead of the large number of BS antennas. Moreover, we compare the proposed quantized feedback technique using the AoD-adaptive subspace codebook with a comparable analog feedback method. Extensive simulations show that the proposed AoD-adaptive subspace codebook achieves good channel feedback quality, while requiring low overhead.

Includes members:
  Member "limited feedback channel estimation in massive mimo with non uniform directional dictionaries":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "low complexity multiuser qam detection for uplink 1 bit massive mimo")
  Member "joint channel training and feedback for fdd massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "downlink pilot precoding and compressed channel feedback for fdd based cell free systems")
  Member "channel estimation for hybrid architecture based wideband millimeter wave systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multiple antenna technologies for beyond 5g")
  Member "compressed channel feedback for correlated massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "high dimensional stochastic gradient quantization for communication efficient edge learning")
  Member "antenna grouping based feedback compression for fdd based massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a covariance based hybrid channel feedback in fdd massive mimo systems")
  Member "the impact of beamwidth on temporal channel variation in vehicular channels and its implications":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "non uniform quantization codebook based hybrid precoding to reduce feedback overhead in millimeter wave mimo systems")
  Member "channel estimation and hybrid precoding for millimeter wave cellular systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback")
  Member "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "dl based csi feedback and cooperative recovery in massive mimo")

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)