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: "parametric bilinear generalized approximate message passing"
  Abstract: We propose a scheme to estimate the parameters   $b_i$   and   $c_j$   of the bilinear form   $z_m=\sum_{i,j} b_i z_m^{(i,j) } c_j$   from noisy measurements   $\{y_m\}_{m=1}^M$  , where   $y_m$   and   $z_m$   are related through an arbitrary likelihood function and   $z_m^{(i,j) }$   are known. Our scheme is based on generalized approximate message passing (G-AMP): it treats   $b_i$   and   $c_j$   as random variables and   $z_m^{(i,j) }$   as an i.i.d. Gaussian 3-way tensor in order to derive a tractable simplification of the sum-product algorithm in the large-system limit. It generalizes previous instances of bilinear G-AMP, such as those that estimate matrices   ${B}$   and   ${C}$   from a noisy measurement of   ${Z}={BC}$  , allowing the application of AMP methods to problems such as self-calibration, blind deconvolution, and matrix compressive sensing. Numerical experiments confirm the accuracy and computational efficiency of the proposed approach.

Connected through hyperedge HE-1 (induced by source paper "bilinear recovery using adaptive vector amp"):
  with members:
    - "regularization by denoising clarifications and new interpretations"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "near optimal matrix recovery from random linear measurements"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "additivity of information in multilayer networks via additive gaussian noise transforms"
    - "hybrid approximate message passing"
    - "state evolution for approximate message passing with non separable functions"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"

Connected through hyperedge HE-2 (induced by source paper "joint channel estimation decoding with frequency selective channels and few bit adcs"):
  with members:
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "blind estimation of sparse broadband massive mimo channels with ideal and one bit adcs"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "phase diagram of matrix compressed sensing"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "sparse channel estimation for massive mimo with 1 bit feedback per dimension"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"

Connected through hyperedge HE-3 (induced by source paper "double sparsity learning based channel and signal estimation in massive mimo with generalized spatial modulation"):
  with members:
    - "bilinear recovery using adaptive vector amp"
    - "super resolution blind channel and signal estimation for massive mimo with arbitrary array geometry"
    - "complete dictionary recovery over the sphere i overview and the geometric picture"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "generalized spatial modulation in large scale multiuser mimo systems"
    - "bilinear generalized approximate message passing part i derivation"
    - "optimal design of energy efficient multi user mimo systems is massive mimo the answer"
    - "scaling up mimo opportunities and challenges with very large arrays"

Connected through hyperedge HE-4 (induced by source paper "passive beamforming and information transfer design for reconfigurable intelligent surfaces aided multiuser mimo systems"):
  with members:
    - "cascaded channel estimation for large intelligent metasurface assisted massive mimo"
    - "passive beamforming and information transfer via large intelligent surface"
    - "smart radio environments empowered by ai reconfigurable meta surfaces an idea whose time has come"
    - "enabling large intelligent surfaces with compressive sensing and deep learning"
    - "towards smart and reconfigurable environment intelligent reflecting surface aided wireless network"
    - "intelligent reflecting surface enhanced wireless network via joint active and passive beamforming"
    - "beyond massive mimo the potential of data transmission with large intelligent surfaces"
    - "reconfigurable intelligent surfaces for energy efficiency in wireless communication"

Connected through hyperedge HE-5 (induced by source paper "near optimal matrix recovery from random linear measurements"):
  with members:
    - "phase diagram of matrix compressed sensing"
    - "an overview of low rank matrix recovery from incomplete observations"
    - "sharp time data tradeoffs for linear inverse problems"
    - "bilinear generalized approximate message passing part i derivation"
    - "low rank modeling and its applications in image analysis"
    - "from denoising to compressed sensing"
    - "living on the edge phase transitions in convex programs with random data"
    - "phase diagram and approximate message passing for blind calibration and dictionary learning"

Connected through hyperedge HE-6 (induced by source paper "bilinear adaptive generalized vector approximate message passing"):
  with members:
    - "bilinear recovery using adaptive vector amp"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "hybrid approximate message passing"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "vector approximate message passing for the generalized linear model"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"
    - "self calibration and biconvex compressive sensing"

Connected through hyperedge HE-7 (induced by source paper "message passing based joint cfo and channel estimation in millimeter wave systems with one bit adcs"):
  with members:
    - "partially blind handovers for mmwave new radio aided by sub 6 ghz lte signaling"
    - "joint cfo and channel estimation in millimeter wave systems with one bit adcs"
    - "a compressive channel estimation technique robust to synchronization impairments"
    - "compressive channel estimation and tracking for large arrays in mm wave picocells"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "channel estimation for hybrid architecture based wideband millimeter wave systems"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"

Connected through hyperedge HE-8 (induced by source paper "plug in estimation in high dimensional linear inverse problems a rigorous analysis"):
  with members:
    - "hybrid approximate message passing"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "state evolution for approximate message passing with non separable functions"
    - "expectation consistent approximate inference generalizations and convergence"
    - "self calibration and biconvex compressive sensing"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "maximin analysis of message passing algorithms for recovering block sparse signals"
    - "hybrid approximate message passing"

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)