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 downlink channel tracking for quantized massive mimo networks"):
  Abstract: This paper proposes a Bayesian downlink channel estimation algorithm for time-varying massive MIMO networks. In particular, the quantization effects at the receiver are considered. In order to fully exploit the sparsity and time correlations of channels, we formulate the time-varying massive MIMO channel as the simultaneously sparse signal model. Then, we propose a sparse Bayesian learning (SBL) framework to learn the model parameters of the sparse virtual channel. To reduce complexity, we employ the expectation maximization (EM) algorithm to achieve the approximated solution. Specifically, the factor graph and the general approximate message passing (GAMP) algorithms are used to compute the desired posterior statistics in the expectation step, so that high-dimensional integrals over the marginal distributions can be avoided. The non-zero supporting vector of a virtual channel is then obtained from channel statistics by a k-means clustering algorithm. After that, the reduced dimensional GAMP based scheme is applied to make the full use of the channel temporal correlation so as to enhance the virtual channel tracking accuracy. Finally, we demonstrate the efficacy of the proposed schemes through simulations.

Includes members:
  Member "beamspace aware adaptive channel estimation for single carrier time varying massive mimo channels":
    also connected through other hyperedges:
      (none)
  Member "interference alignment and soft space reuse based cooperative transmission for multi cell massive mimo networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "downlink secrecy rate of one bit massive mimo system with active eavesdropping")
  Member "bayesian optimal data detector for mmwave ofdm system with low resolution adc":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "bayesian optimal data detector for hybrid mmwave mimo ofdm systems with low resolution adcs")
  Member "a gamp based low complexity sparse bayesian learning algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "passive beamforming and information transfer design for reconfigurable intelligent surfaces aided multiuser mimo systems")
  Member "mixed adc massive mimo detectors performance analysis and design optimization":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "performance analysis of quantized uplink massive mimo ofdm with oversampling under adjacent channel interference")
  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 "training sequence design for feedback assisted hybrid beamforming in massive mimo systems":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "time varying massive mimo channel estimation capturing reconstruction and restoration")

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)