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 "matrix completion from quantized samples via generalized sparse bayesian learning"):
  Abstract: The recovery of a low rank matrix from a subset of noisy low-precision quantized samples arises in several applications such as collaborative filtering, intelligent recommendation and millimeter wave channel estimation with few bit ADCs. In this paper, a generalized sparse Bayesian learning (Gr-SBL) combining expectation propagation (EP) is proposed to solve the matrix completion (MC), which is termed as MC-Gr-SBL. The MC-Gr-SBL automatically estimates the rank, the factors and their covariance matrices, and the noise variance. In addition, MC-Gr-SBL is proposed to solve the two dimensional line spectral estimation problem by incorporating the MUSIC algorithm. Finally, substantial numerical experiments are conducted to verify the effectiveness of the proposed algorithm.

Includes members:
  Member "a unified bayesian inference framework for generalized linear models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a new insight into gamp and amp")
  Member "fast low rank bayesian matrix completion with hierarchical gaussian prior models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the sparse reverse of principal component analysis for fast low rank matrix completion")
  Member "millimeter wave channel estimation via exploiting joint sparse and low rank structures":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep learning for massive mimo channel state acquisition and feedback")
  Member "bayes optimal joint channel and data estimation for massive mimo with low precision adcs":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "faster activity and data detection in massive random access a multi armed bandit approach")
  Member "low rank tensor decomposition aided channel estimation for millimeter wave mimo ofdm systems":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "generalized approximate message passing for massive mimo mmwave channel estimation with laplacian prior")
  Member "channel estimation in broadband millimeter wave mimo systems with few bit adcs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "capacity bounds for communication systems with quantization and spectral constraints")
  Member "categorical matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "an overview of low rank matrix recovery from incomplete observations")
  Member "matrix completion from a few entries":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")

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)