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: "parameterless optimal approximate message passing"
  Abstract: Iterative thresholding algorithms are well-suited for high-dimensional problems in sparse recovery and compressive sensing. The performance of this class of algorithms depends heavily on the tuning of certain threshold parameters. In particular, both the final reconstruction error and the convergence rate of the algorithm crucially rely on how the threshold parameter is set at each step of the algorithm. In this paper, we propose a parameter-free approximate message passing (AMP) algorithm that sets the threshold parameter at each iteration in a fully automatic way without either having an information about the signal to be reconstructed or needing any tuning from the user. We show that the proposed method attains both the minimum reconstruction error and the highest convergence rate. Our method is based on applying the Stein unbiased risk estimate (SURE) along with a modified gradient descent to find the optimal threshold in each iteration. Motivated by the connections between AMP and LASSO, it could be employed to find the solution of the LASSO for the optimal regularization parameter. To the best of our knowledge, this is the first work concerning parameter tuning that obtains the fastest convergence rate with theoretical guarantees.

Connected through hyperedge HE-1 (induced by source paper "beamspace channel estimation for massive mimo mmwave systems algorithm and vlsi design"):
  with members:
    - "mmwave channel estimation via atomic norm minimization for multi user hybrid precoding"
    - "throughput analysis of massive mimo uplink with low resolution adcs"
    - "channel estimation and performance analysis of one bit massive mimo systems"
    - "newtonized orthogonal matching pursuit frequency estimation over the continuum"
    - "hybrid digital and analog beamforming design for large scale antenna arrays"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"
    - "limited feedback hybrid precoding for multi user millimeter wave systems"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"

Connected through hyperedge HE-2 (induced by source paper "hybrid approximate message passing"):
  with members:
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "decoding from pooled data phase transitions of message passing"
    - "random access in c ran for user activity detection with limited capacity fronthaul"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "expectation consistent approximate inference generalizations and convergence"
    - "sparse multinomial logistic regression via approximate message passing"
    - "a survey of stochastic simulation and optimization methods in signal processing"
    - "learning and free energies for vector approximate message passing"

Connected through hyperedge HE-3 (induced by source paper "empirical bayes estimators for high dimensional sparse vectors"):
  with members:
    - "finite sample analysis of approximate message passing algorithms"
    - "cluster seeking james stein estimators"
    - "near optimal compressed sensing without priors parametric sure 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"
    - "graphical models concepts in compressed sensing"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "sparse multinomial logistic regression via approximate message passing"):
  with members:
    - "hybrid approximate message passing"
    - "binary linear classification and feature selection via generalized approximate message passing"
    - "on the convergence of approximate message passing with arbitrary matrices"
    - "fixed points of generalized approximate message passing with arbitrary matrices"
    - "expectation maximization gaussian mixture approximate message passing"
    - "robust 1 bit compressed sensing and sparse logistic regression a convex programming approach"
    - "hybrid approximate message passing"
    - "hybrid approximate message passing with applications to structured sparsity"

Connected through hyperedge HE-5 (induced by source paper "on the performance of mismatched data detection in large mimo systems"):
  with members:
    - "optimality of large mimo detection via approximate message passing"
    - "ber analysis of the box relaxation for bpsk signal recovery"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "estimation in gaussian noise properties of the minimum mean square error"
    - "message passing algorithms for compressed sensing"
    - "randomly spread cdma asymptotics via statistical physics"

Connected through hyperedge HE-6 (induced by source paper "compressive imaging via approximate message passing with image denoising"):
  with members:
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "signal estimation with additive error metrics in compressed sensing"
    - "empirical bayes and full bayes for signal estimation"
    - "from denoising to compressed sensing"
    - "sparse estimation with the swept approximated message passing algorithm"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "compressive imaging using approximate message passing and a markov tree prior"
    - "model based compressive sensing"

Connected through hyperedge HE-7 (induced by source paper "a deep learning approach to structured signal recovery"):
  with members:
    - "imagenet large scale visual recognition challenge"
    - "from denoising to compressed sensing"
    - "near optimal adaptive compressed sensing"
    - "message passing algorithms for compressed sensing"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "from denoising to compressed sensing"):
  with members:
    - "compressive imaging via approximate message passing with image denoising"
    - "does ell_p minimization outperform ell_1 minimization"
    - "image compressive sensing recovery using adaptively learned sparsifying basis via l0 minimization"
    - "bilinear generalized approximate message passing part i derivation"
    - "s amp approximate message passing for general matrix ensembles"
    - "signal recovery using expectation consistent approximation for linear observations"
    - "generalized approximate message passing for cosparse analysis compressive sensing"
    - "from compression to 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)