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: "a deep generative deconvolutional image model"
  Abstract: A deep generative model is developed for representation and analysis of images, based on a hierarchical convolutional dictionary-learning framework. Stochastic {\em unpooling} is employed to link consecutive layers in the model, yielding top-down image generation. A Bayesian support vector machine is linked to the top-layer features, yielding max-margin discrimination. Deep deconvolutional inference is employed when testing, to infer the latent features, and the top-layer features are connected with the max-margin classifier for discrimination tasks. The model is efficiently trained using a Monte Carlo expectation-maximization (MCEM) algorithm, with implementation on graphical processor units (GPUs) for efficient large-scale learning, and fast testing. Excellent results are obtained on several benchmark datasets, including ImageNet, demonstrating that the proposed model achieves results that are highly competitive with similarly sized convolutional neural networks.

Connected through hyperedge HE-1 (induced by source paper "learning representations by maximizing mutual information in variational autoencoder"):
  with members:
    - "spherical latent spaces for stable variational autoencoders"
    - "recent advances in autoencoder based representation learning"
    - "mine mutual information neural estimation"
    - "adversarial symmetric variational autoencoder"
    - "adversarially regularized autoencoders"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "fixing a broken elbo"
    - "towards deeper understanding of variational autoencoding models"

Connected through hyperedge HE-2 (induced by source paper "applications of generative adversarial models in visual search reformulation"):
  with members:
    - "a style based generator architecture for generative adversarial networks"
    - "web scale responsive visual search at bing"
    - "attgan facial attribute editing by only changing what you want"
    - "triangle generative adversarial networks"
    - "cycada cycle consistent adversarial domain adaptation"
    - "visual search at ebay"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "visual discovery at pinterest"

Connected through hyperedge HE-3 (induced by source paper "adversarial symmetric variational autoencoder"):
  with members:
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "triangle generative adversarial networks"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "deconvolutional latent variable model for text sequence matching"
    - "triple generative adversarial nets"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "vae learning via stein variational gradient descent"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "vae learning via stein variational gradient descent"):
  with members:
    - "adversarial symmetric variational autoencoder"
    - "deconvolutional latent variable model for text sequence matching"
    - "deconvolutional paragraph representation learning"
    - "variational inference for monte carlo objectives"
    - "pixel recurrent neural networks"
    - "importance weighted autoencoders"
    - "neural variational inference for text processing"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "generative discriminative variational model for visual recognition"):
  with members:
    - "semantic image inpainting with deep generative models"
    - "an uncertain future forecasting from static images using variational autoencoders"
    - "reducing overfitting in deep networks by decorrelating representations"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "very deep convolutional networks for large scale image recognition"
    - "sequence to sequence learning with neural networks"
    - "zero shot learning through cross modal transfer"

Connected through hyperedge HE-6 (induced by source paper "infinite variational autoencoder for semi supervised learning"):
  with members:
    - "semi supervised vocabulary informed learning"
    - "semi supervised learning with explicit relationship regularization"
    - "watch and learn semi supervised learning of object detectors from videos"
    - "an infinite restricted boltzmann machine"
    - "deep residual learning for image recognition"
    - "semi supervised learning with ladder networks"
    - "autoencoding beyond pixels using a learned similarity metric"
    - "3d shapenets a deep representation for volumetric shapes"

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)