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: "accurate prediction of phase transitions in compressed sensing via a connection to minimax denoising"
  Abstract: Compressed sensing posits that, within limits, one can undersample a sparse signal and yet reconstruct it accurately. Knowing the precise limits to such undersampling is important both for theory and practice. We present a formula that characterizes the allowed undersampling of generalized sparse objects. The formula applies to Approximate Message Passing (AMP) algorithms for compressed sensing, which are here generalized to employ denoising operators besides the traditional scalar soft thresholding denoiser. This paper gives several examples including scalar denoisers not derived from convex penalization -- the firm shrinkage nonlinearity and the minimax nonlinearity -- and also nonscalar denoisers -- block thresholding, monotone regression, and total variation minimization. #R##N#Let the variables eps = k/N and delta = n/N denote the generalized sparsity and undersampling fractions for sampling the k-generalized-sparse N-vector x_0 according to y=Ax_0. Here A is an n\times N measurement matrix whose entries are iid standard Gaussian. The formula states that the phase transition curve delta = delta(eps) separating successful from unsuccessful reconstruction of x_0 by AMP is given by: delta = M(eps| Denoiser), where M(eps| Denoiser) denotes the per-coordinate minimax mean squared error (MSE) of the specified, optimally-tuned denoiser in the directly observed problem y = x + z. In short, the phase transition of a noiseless undersampling problem is identical to the minimax MSE in a denoising problem.

Connected through hyperedge HE-1 (induced by source paper "living near the edge a lower bound on the phase transition of total variation minimization"):
  with members:
    - "on the error in phase transition computations for compressed sensing"
    - "sample complexity of total variation minimization"
    - "total variation minimization in compressed sensing"
    - "ell 1 analysis minimization and generalized co sparsity when does recovery succeed"
    - "precise phase transition of total variation minimization"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "robust analysis l1 recovery from gaussian measurements and total variation minimization"
    - "corrupted sensing novel guarantees for separating structured signals"

Connected through hyperedge HE-2 (induced by source paper "exploiting prior information in block sparse signals"):
  with members:
    - "maximum a posteriori signal recovery with prior information applications to compressive sensing"
    - "performance limits for noisy multimeasurement vector problems"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "spectral super resolution with prior knowledge"
    - "living on the edge phase transitions in convex programs with random data"
    - "accelerating multi echo t2 weighted mr imaging analysis prior group sparse optimization"
    - "recovering compressively sampled signals using partial support information"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

Connected through hyperedge HE-3 (induced by source paper "near optimal matrix recovery from random linear measurements"):
  with members:
    - "phase diagram of matrix compressed sensing"
    - "parametric bilinear generalized approximate message passing"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "regularization by denoising clarifications and new interpretations"):
  with members:
    - "local kernels that approximate bayesian regularization and proximal operators"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "scene adapted plug and play algorithm with guaranteed convergence applications to data fusion in imaging"
    - "an online plug and play algorithm for regularized image reconstruction"
    - "acceleration of red via vector extrapolation"
    - "trainable nonlinear reaction diffusion a flexible framework for fast and effective image restoration"
    - "hybrid approximate message passing"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"

Connected through hyperedge HE-5 (induced by source paper "the committee machine computational to statistical gaps in learning a two layers neural network"):
  with members:
    - "overlap matrix concentration in optimal bayesian inference"
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "mutual information for low rank even order symmetric tensor factorization"
    - "rethinking generalization requires revisiting old ideas statistical mechanics approaches and complex learning behavior"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "decoding from pooled data phase transitions of message passing"
    - "performance limits for noisy multimeasurement vector problems"
    - "spurious local minima are common in two layer relu neural networks"

Connected through hyperedge HE-6 (induced by source paper "sparse activity detection for massive connectivity"):
  with members:
    - "capacity of gaussian many access channels"
    - "compressive random access using a common overloaded control channel"
    - "sparse signal processing concepts for efficient 5g system design"
    - "what will 5g be"
    - "efficient high dimensional inference in the multiple measurement vector problem"
    - "belief propagation for joint sparse recovery"
    - "asymptotic analysis of complex lasso via complex approximate message passing camp"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "convex relaxations of convolutional neural nets"):
  with members:
    - "learning one convolutional layer with overlapping patches"
    - "lifted neural networks"
    - "gradient descent learns one hidden layer cnn don t be afraid of spurious local minima"
    - "learning relus via gradient descent"
    - "globally optimal gradient descent for a convnet with gaussian inputs"
    - "reliably learning the relu in polynomial time"
    - "convexified convolutional neural networks"
    - "randomized sketches of convex programs with sharp guarantees"

Connected through hyperedge HE-8 (induced by source paper "on the error in phase transition computations for compressed sensing"):
  with members:
    - "exploiting prior information in block sparse signals"
    - "ell 1 analysis minimization and generalized co sparsity when does recovery succeed"
    - "precise phase transition of total variation minimization"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "living on the edge phase transitions in convex programs with random data"
    - "sharp mse bounds for proximal denoising"
    - "simple bounds for recovering low complexity models"
    - "various thresholds for ell_1 optimization in 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)