human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "sparse signal recovery using generalized approximate message passing with built in parameter estimation"
  Abstract: The generalized approximate message passing (GAMP) algorithm under the Bayesian setting shows advantage in recovering under-sampled sparse signals from corrupted observations. Compared to conventional convex optimization methods, it has a much lower complexity and is computationally tractable. In the GAMP framework, the sparse signal and the observation are viewed to be generated according to some pre-specified probability distributions in the input and output channels. However, the parameters of the distributions are usually unknown in practice. In this paper, we propose an extended GAMP algorithm with built-in parameter estimation (PE-GAMP) and present its empirical convergence analysis. PE-GAMP treats the parameters as unknown random variables with simple priors and jointly estimates them with the sparse signals. Compared with Expectation Maximization (EM) based parameter estimation methods, the proposed PE-GAMP could draw information from the prior distributions of the parameters to perform parameter estimation. It is also more robust and much simpler, which enables us to consider more complex signal distributions apart from the usual Bernoulli-Gaussian (BGm) mixture distribution. Specifically, the formulations of Bernoulli-Exponential mixture (BEm) distribution and Laplace distribution are given in this paper. Simulated noiseless sparse signal recovery experiments demonstrate that the performance of the proposed PE-GAMP matches the oracle GAMP algorithm. When noise is present, both the simulated experiments and the real image recovery experiments show that PE-GAMP is still able to maintain its robustness and outperform EM based parameter estimation method when the sampling ratio is small. Additionally, using the BEm formulation of the PE-GAMP, we can successfully perform non-negative sparse coding of local image patches and provide useful features for the image classification task.

Connected through hyperedge HE-1 (induced by source paper "sparse signal recovery via generalized entropy functions minimization"):
  with members:
    - "sparsity averaging for compressive imaging"
    - "oblique pursuits for compressed sensing"
    - "fast and efficient compressive sensing using structurally random matrices"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing"
    - "robust principal component analysis"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "beam on graph simultaneous channel estimation for mmwave mimo systems with multiple users"):
  with members:
    - "fast channel estimation for millimetre wave wireless systems using overlapped beam patterns"
    - "multi user precoding and channel estimation for hybrid millimeter wave systems"
    - "millimeter wave mimo channel estimation using overlapped beam patterns and rate adaptation"
    - "race a rate adaptive channel estimation approach for millimeter wave mimo systems"
    - "fountain code inspired channel estimation for multi user millimeter wave mimo systems"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "massive mimo ten myths and one critical question"
    - "limited feedback hybrid precoding for multi user millimeter wave systems"

Connected through hyperedge HE-3 (induced by source paper "beam on graph simultaneous channel estimation in multi user millimeter wave mimo systems"):
  with members:
    - "fast channel estimation for millimetre wave wireless systems using overlapped beam patterns"
    - "multi user precoding and channel estimation for hybrid millimeter wave systems"
    - "millimeter wave mimo channel estimation using overlapped beam patterns and rate adaptation"
    - "race a rate adaptive channel estimation approach for millimeter wave mimo systems"
    - "fountain code inspired channel estimation for multi user millimeter wave mimo systems"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "massive mimo ten myths and one critical question"
    - "limited feedback hybrid precoding for multi user millimeter wave systems"

Question: Please tell me which class the center node 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)