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 "uplink downlink channel covariance transformations and precoding design for fdd massive mimo"):
  Abstract: A large majority of cellular networks deployed today make use of Frequency Division Duplexing (FDD) where, in contrast with Time Division Duplexing (TDD), the channel reciprocity does not hold and explicit downlink (DL) probing and uplink (UL) feedback are needed in order to achieve spatial multiplexing gain. To support massive MIMO, the overhead incurred by conventional DL probing and UL feedback schemes scales linearly with the number of BS antennas and, therefore, may be very large. In this paper, we present a new approach to achieve a very competitive trade-off between spatial multiplexing gain and probing-feedback overhead in such systems. Our approach is based on two novel methods: (i) an efficient regularization technique based on Deep Neural Networks (DNN) that learns the Angular Spread Function (ASF) of users channels and permits to estimate the DL covariance matrix from the noisy i.i.d. channel observations obtained freely via UL pilots (UL-DL covariance transformation), (ii) a novel "sparsifying precoding" technique that uses the estimated DL covariance matrix from (i) and imposes a controlled sparsity on the DL channel such that given any assigned DL pilot dimension, it is able to find an optimal sparsity level and a corresponding sparsifying precoder for which the "effective" channel vectors after sparsification can be estimated at the BS with a low mean-square error. We compare our proposed DNN-based method in (i) with other methods in the literature via numerical simulations and show that it yields a very competitive performance. We also compare our sparsifying precoder in (ii) with the state-of-the-art statistical beamforming methods under the assumption that those methods also have access to the covariance knowledge in the DL and show that our method yields higher spectral efficiency since it uses in addition the instantaneous channel information after sparsification.

Includes members:
  Member "deep learning methods for improved decoding of linear codes":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "perm2vec graph permutation selection for decoding of error correction codes using self attention")
  Member "5g new radio unveiling the essentials of the next generation wireless access technology":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "antenna placement and performance tradeoffs with hand blockage in millimeter wave systems")
  Member "fdd massive mimo via ul dl channel covariance extrapolation and active channel sparsification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "energy efficiency optimization for downlink massive mimo with statistical csit")
  Member "deep learning for joint source channel coding of text":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an adaptive deep learning algorithm based autoencoder for interference channels")
  Member "low complexity massive mimo subspace estimation and tracking from low dimensional projections":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "an efficient beam and channel acquisition via sparsity map and joint angle delay power profile estimation for wideband massive mimo systems")
  Member "channel covariance estimation in massive mimo frequency division duplex systems":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "passive radar at the roadside unit to configure millimeter wave vehicle to infrastructure links")
  Member "on the asymptotic equivalence of circulant and toeplitz matrices":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bounding singular values of convolution layers")
  Member "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "deep learning based fdd non stationary massive mimo downlink channel reconstruction")

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)