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 "efficient downlink channel reconstruction for fdd multi antenna systems"):
  Abstract: In this paper, we propose an efficient downlink channel reconstruction scheme for a frequency-division-duplex multi-antenna system by utilizing uplink channel state information combined with limited feedback. Based on the spatial reciprocity in a wireless channel, the downlink channel is reconstructed by using frequency-independent parameters. We first estimate the gains, delays, and angles during uplink sounding. The gains are then refined through downlink training and sent back to the base station (BS). With limited overhead, the refinement can substantially improve the accuracy of the downlink channel reconstruction. The BS can then reconstruct the downlink channel with the uplink-estimated delays and angles and the downlink-refined gains. We also introduce and extend the Newtonized orthogonal matching pursuit (NOMP) algorithm to detect the delays and gains in a multi-antenna multi-subcarrier condition. The results of our analysis show that the extended NOMP algorithm achieves high estimation accuracy. Simulations and over-the-air tests are performed to assess the performance of the efficient downlink channel reconstruction scheme. The results show that the reconstructed channel is close to the practical channel and that the accuracy is enhanced when the number of BS antennas increases, thereby highlighting that the promising application of the proposed scheme in large-scale antenna array systems.

Includes members:
  Member "multi band covariance interpolation with applications in massive mimo":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep learning based fdd non stationary massive mimo downlink channel reconstruction")
  Member "channel feedback based on aod adaptive subspace codebook in fdd massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a covariance based hybrid channel feedback in fdd massive mimo systems")
  Member "fdd massive mimo efficient downlink probing and uplink feedback via active channel sparsification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "on optimal scheduling for joint spatial division and multiplexing approach in fdd massive mimo")
  Member "channel estimation for tdd fdd massive mimo systems with channel covariance computing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning based fdd non stationary massive mimo downlink channel reconstruction")
  Member "newtonized orthogonal matching pursuit frequency estimation over the continuum":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep learning based fdd non stationary massive mimo downlink channel reconstruction")
  Member "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "deep learning based fdd non stationary massive mimo downlink channel reconstruction")
  Member "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dl based csi feedback and cooperative recovery in massive mimo")
  Member "massive mimo for next generation wireless systems":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "joint power control and lsfd for wireless powered cell free 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)