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: "approximate message passing with unitary transformation"
  Abstract: Approximate message passing (AMP) and its variants, developed based on loopy belief propagation, are attractive for estimating a vector x from a noisy version of z = Ax, which arises in many applications. For a large A with i. i. d. elements, AMP can be characterized by the state evolution and exhibits fast convergence. However, it has been shown that, AMP mayeasily diverge for a generic A. In this work, we develop a new variant of AMP based on a unitary transformation of the original model (hence the variant is called UT-AMP), where the unitary matrix is available for any matrix A, e.g., the conjugate transpose of the left singular matrix of A, or a normalized DFT (discrete Fourier transform) matrix for any circulant A. We prove that, in the case of Gaussian priors, UT-AMP always converges for any matrix A. It is observed that UT-AMP is much more robust than the original AMP for difficult A and exhibits fast convergence. #R##N#A special form of UT-AMP with a circulant A was used in our previous work [13] for turbo equalization. This work extends it to a generic A, and provides a theoretical investigation on the convergence.

Connected through hyperedge HE-1 (induced by source paper "sparse bayesian learning using approximate message passing with unitary transformation"):
  with members:
    - "a gamp based low complexity sparse bayesian learning algorithm"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "on the convergence of approximate message passing with arbitrary matrices"
    - "sparse estimation with the swept approximated message passing algorithm"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "iterative channel estimation using lse and sparse message passing for mmwave mimo systems"):
  with members:
    - "low complexity sparse bayesian learning using combined belief propagation and mean field with a stretched factor graph"
    - "beamspace channel estimation for millimeter wave massive mimo systems with lens antenna array"
    - "a lse and sparse message passing based channel estimation for mmwave mimo systems"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "low rf complexity technologies to enable millimeter wave mimo with large antenna array for 5g wireless communications"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "convergence analysis and assurance for gaussian message passing iterative detector in massive mu mimo systems"
    - "frequency selective hybrid precoding for limited feedback 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)