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: "density modeling of images using a generalized normalization transformation"
  Abstract: We introduce a parametric nonlinear transformation that is well-suited for Gaussianizing data from natural images. The data are linearly transformed, and each component is then normalized by a pooled activity measure, computed by exponentiating a weighted sum of rectified and exponentiated components and a constant. We optimize the parameters of the full transformation (linear transform, exponents, weights, constant) over a database of natural images, directly minimizing the negentropy of the responses. The optimized transformation substantially Gaussianizes the data, achieving a significantly smaller mutual information between transformed components than alternative methods including ICA and radial Gaussianization. The transformation is differentiable and can be efficiently inverted, and thus induces a density model on images. We show that samples of this model are visually similar to samples of natural image patches. We demonstrate the use of the model as a prior probability density that can be used to remove additive noise. Finally, we show that the transformation can be cascaded, with each layer optimized using the same Gaussianization objective, thus offering an unsupervised method of optimizing a deep network architecture.

Connected through hyperedge HE-1 (induced by source paper "active fine tuning from gmad examples improves blind image quality assessment"):
  with members:
    - "i am going mad maximum discrepancy competition for comparing classifiers adaptively"
    - "learning to blindly assess image quality in the laboratory and wild"
    - "deep neural networks for no reference and full reference image quality assessment"
    - "nima neural image assessment"
    - "on the use of deep learning for blind image quality assessment"
    - "pieapp perceptual image error assessment through pairwise preference"
    - "rankiqa learning from rankings for no reference image quality assessment"
    - "dipiq blind image quality assessment by learning to rank discriminable image pairs"

Connected through hyperedge HE-2 (induced by source paper "be like water robustness to extraneous variables via adaptive feature normalization"):
  with members:
    - "filter response normalization layer eliminating batch dependence in the training of deep neural networks"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "two at once enhancing learning and generalization capacities via ibn net"
    - "adversarial discriminative domain adaptation"
    - "instance normalization the missing ingredient for fast stylization"
    - "end to end optimized image compression"
    - "deep transfer learning with joint adaptation networks"
    - "densely connected convolutional networks"

Connected through hyperedge HE-3 (induced by source paper "deep joint transmission recognition for power constrained iot devices"):
  with members:
    - "bottlenet an end to end approach for feature compression in device edge co inference systems"
    - "deep joint source channel coding for wireless image retrieval"
    - "improving device edge cooperative inference of deep learning via 2 step pruning"
    - "bottlenet a deep learning architecture for intelligent mobile cloud computing services"
    - "deepjscc f deep joint source channel coding of images with feedback"
    - "deep joint source channel coding for wireless image transmission"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "end to end optimized image compression"

Connected through hyperedge HE-4 (induced by source paper "binocular rivalry oriented predictive auto encoding network for blind stereoscopic image quality assessment"):
  with members:
    - "stereoscopic omnidirectional image quality assessment based on predictive coding theory"
    - "nima neural image assessment"
    - "end to end optimized image compression"
    - "identity mappings in deep residual networks"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep residual learning for image recognition"
    - "how transferable are features in deep neural networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-5 (induced by source paper "sliced wasserstein generative models"):
  with members:
    - "video to video synthesis"
    - "improving the improved training of wasserstein gans a consistency term and its dual effect"
    - "spectral normalization for generative adversarial networks"
    - "improving gans using optimal transport"
    - "banach wasserstein gan"
    - "generative modeling using the sliced wasserstein distance"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-6 (induced by source paper "frank wolfe network an interpretable deep structure for non sparse coding"):
  with members:
    - "learning simple thresholded features with sparse support recovery"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "ista net interpretable optimization inspired deep network for image compressive sensing"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "a unified optimization view on generalized matching pursuit and frank wolfe"
    - "maximal sparsity with deep networks"
    - "learning optimal nonlinearities for iterative thresholding algorithms"
    - "normalizing the normalizers comparing and extending network normalization schemes"

Connected through hyperedge HE-7 (induced by source paper "deepjscc f deep joint source channel coding of images with feedback"):
  with members:
    - "successive refinement of images with deep joint source channel coding"
    - "deep learning based polar code design"
    - "joint autoregressive and hierarchical priors for learned image compression"
    - "end to end deep learning of optical fiber communications"
    - "deepcode feedback codes via deep learning"
    - "deep joint source channel coding for wireless image transmission"
    - "deep learning methods for improved decoding of linear codes"
    - "deep learning for joint source channel coding of text"

Connected through hyperedge HE-8 (induced by source paper "rate distortion optimization guided autoencoder for generative approach with quantitatively measurable latent space"):
  with members:
    - "exact rate distortion in autoencoders via echo noise"
    - "adversarially learned anomaly detection"
    - "recent advances in autoencoder based representation learning"
    - "variational autoencoders pursue pca directions by accident"
    - "neural discrete representation learning"
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "adam a method for stochastic optimization"
    - "nice non linear independent components estimation"

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.LG(Machine Learning)