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: "an approximate message passing framework for side information"
  Abstract: Approximate message passing (AMP) methods have gained recent traction in sparse signal recovery. Additional information about the signal, or  side information  (SI), is commonly available and can aid in efficient signal recovery. This paper presents an AMP-based framework that exploits SI and can be readily implemented in various settings for which the SI results in separable distributions. To illustrate the simplicity and applicability of our approach, this framework is applied to a Bernoulli–Gaussian (BG) model and a time-varying birth-death-drift (BDD) signal model, motivated by applications in channel estimation. We develop a suite of algorithms, called AMP-SI, and derive denoisers for the BDD and BG models. Numerical evidence demonstrating the advantages of our approach are presented alongside empirical evidence of the accuracy of a proposed state evolution.

Connected through hyperedge HE-1 (induced by source paper "deep coupled representation learning for sparse linear inverse problems with side information"):
  with members:
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "amp inspired deep networks for sparse linear inverse problems"
    - "multimodal image super resolution via joint sparse representations induced by coupled dictionaries"
    - "deep learning sparse ternary projections for compressed sensing of images"
    - "multi modal dictionary learning for image separation with application in art investigation"
    - "tradeoffs between convergence speed and reconstruction accuracy in inverse problems"
    - "from denoising to compressed sensing"
    - "deep unfolding model based inspiration of novel deep architectures"

Connected through hyperedge HE-2 (induced by source paper "passive radar at the roadside unit to configure millimeter wave vehicle to infrastructure links"):
  with members:
    - "spatial covariance estimation for millimeter wave hybrid systems using out of band information"
    - "fdd massive mimo via ul dl channel covariance extrapolation and active channel sparsification"
    - "spatial channel covariance estimation for the hybrid mimo architecture a compressive sensing based approach"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "millimeter wave beam selection using out of band spatial information"
    - "channel covariance estimation in massive mimo frequency division duplex systems"
    - "frequency selective hybrid precoding for limited feedback millimeter wave systems"
    - "hybrid beamforming for massive mimo a survey"

Connected through hyperedge HE-3 (induced by source paper "an analysis of state evolution for approximate message passing with side information"):
  with members:
    - "finite sample analysis of approximate message passing algorithms"
    - "state evolution for approximate message passing with non separable functions"
    - "approximate message passing based compressed sensing reconstruction with generalized elastic net prior"
    - "dynamic compressive sensing of time varying signals via approximate message passing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing"

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)