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 "time varying massive mimo channel estimation capturing reconstruction and restoration"):
  Abstract: On the time-varying channel estimation, the traditional downlink (DL) channel restoration schemes usually require the reconstruction for the covariance of downlink process noise vector, which is dependent on DL channel covariance matrix (CCM). However, the acquisition of the CCM leads to unacceptable overhead in massive MIMO systems. To tackle this problem, in this paper, we propose a novel scheme for the DL channel tracking. First, with the help of virtual channel representation (VCR), we build a dynamic uplink (UL) massive MIMO channel model with the consideration of off-grid refinement. Then, a coordinate-wise maximization based expectation maximization (EM) algorithm is adopted for capturing the model parameters, including the spatial signatures, the time-correlation factors, the off-grid bias, the channel power, and the noise power. Thanks to the angle reciprocity, the spatial signatures, timecorrelation factors and off-grid bias of the DL channel model can be reconstructed with the knowledge of UL ones. However, the other two kinds of model parameters are closely related with the carrier frequency, which cannot be perfectly inferred from the UL ones. Instead of relearning the DL model parameters with dedicated training, we resort to the optimal Bayesian Kalman filter (OBKF) method to accurately track the DL channel with the partially prior knowledge. At the same time, the model parameters will be gradually restored. Specially, the factor-graph and the Metropolis Hastings MCMC are utilized within the OBKF framework. Finally, numerical results are provided to demonstrate the efficiency of our proposed scheme.

Includes members:
  Member "efficient downlink channel reconstruction for fdd multi antenna systems":
    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 estimation for tdd fdd massive mimo systems with channel covariance computing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep learning based fdd non stationary massive mimo downlink channel reconstruction")
  Member "interference alignment and soft space reuse based cooperative transmission for multi cell massive mimo networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "downlink secrecy rate of one bit massive mimo system with active eavesdropping")
  Member "channel acquisition for massive mimo ofdm with adjustable phase shift pilots":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "energy efficiency optimization for downlink massive mimo with statistical csit")
  Member "design and implementation of a tdd based 128 antenna massive mimo prototyping system":
    also connected through other hyperedges:
      (none)
  Member "uplink achievable rate for massive mimo systems with low resolution adc":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "asymptotic regime analysis of noma uplink networks under qos delay constraints")
  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 "millimeter wave mimo with lens antenna array a new path division multiplexing paradigm":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "multiple antenna technologies for beyond 5g")

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)