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: "revisiting bayesian blind deconvolution"
  Abstract: Blind deconvolution involves the estimation of a sharp signal or image given only a blurry observation. Because this problem is fundamentally ill-posed, strong priors on both the sharp image and blur kernel are required to regularize the solution space. While this naturally leads to a standard MAP estimation framework, performance is compromised by unknown trade-off parameter settings, optimization heuristics, and convergence issues stemming from non-convexity and/or poor prior selections. To mitigate some of these problems, a number of authors have recently proposed substituting a variational Bayesian (VB) strategy that marginalizes over the high-dimensional image space leading to better estimates of the blur kernel. However, the underlying cost function now involves both integrals with no closed-form solution and complex, function-valued arguments, thus losing the transparency of MAP. Beyond standard Bayesian-inspired intuitions, it thus remains unclear by exactly what mechanism these methods are able to operate, rendering understanding, improvements and extensions more difficult. To elucidate these issues, we demonstrate that the VB methodology can be recast as an unconventional MAP problem with a very particular penalty/prior that couples the image, blur kernel, and noise level in a principled way. This unique penalty has a number of useful characteristics pertaining to relative concavity, local minima avoidance, and scale-invariance that allow us to rigorously explain the success of VB including its existing implementational heuristics and approximations. It also provides strict criteria for choosing the optimal image prior that, perhaps counter-intuitively, need not reflect the statistics of natural scenes. In so doing we challenge the prevailing notion of why VB is successful for blind deconvolution while providing a transparent platform for introducing enhancements.

Connected through hyperedge HE-1 (induced by source paper "on the global geometry of sphere constrained sparse blind deconvolution"):
  with members:
    - "guaranteed blind sparse spikes deconvolution via lifting and convex optimization"
    - "rapid robust and reliable blind deconvolution via nonconvex optimization"
    - "a geometric analysis of phase retrieval"
    - "complete dictionary recovery over the sphere"
    - "sparse modeling for image and vision processing"
    - "blind deconvolution using convex programming"
    - "exact recovery of sparsely used dictionaries"

Connected through hyperedge HE-2 (induced by source paper "compressing neural networks using the variational information bottleneck"):
  with members:
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "more is less a more complicated network with less inference complexity"
    - "learning structured sparsity in deep neural networks"
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "deep variational information bottleneck"
    - "dropneuron simplifying the structure of deep neural networks"
    - "binaryconnect training deep neural networks with binary weights during propagations"

Connected through hyperedge HE-3 (induced by source paper "discovery of latent factors in high dimensional data using tensor methods"):
  with members:
    - "scalable latent tree model and its application to health analytics"
    - "skip thought vectors"
    - "self adaptive hierarchical sentence model"
    - "convolutional dictionary learning through tensor factorization"
    - "improved semantic representations from tree structured long short term memory networks"
    - "towards universal paraphrastic sentence embeddings"
    - "escaping from saddle points online stochastic gradient for tensor decomposition"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-4 (induced by source paper "convergence analysis of map based blur kernel estimation"):
  with members:
    - "blind deconvolution with non local sparsity reweighting"

Connected through hyperedge HE-5 (induced by source paper "convolutional dictionary learning through tensor factorization"):
  with members:
    - "optimization methods for convolutional sparse coding"
    - "a convolutional neural network for modelling sentences"
    - "blind deconvolution with non local sparsity reweighting"
    - "new algorithms for learning incoherent and overcomplete dictionaries"
    - "blind deconvolution using convex programming"
    - "tensor decompositions for learning latent variable models"

Connected through hyperedge HE-6 (induced by source paper "simple accurate and robust nonparametric blind super resolution"):
  with members:
    - "bi l0 l2 norm regularization for blind motion deblurring"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "learning to deblur"):
  with members:
    - "learning a convolutional neural network for non uniform motion blur removal"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-8 (induced by source paper "blind deconvolution with non local sparsity reweighting"):
  with members:
    - "blind deconvolution using convex programming"

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.CV(Computer Vision and Pattern Recognition)