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: "texture modeling with convolutional spike and slab rbms and deep extensions"
  Abstract: We apply the spike-and-slab Restricted Boltzmann Machine (ssRBM) to texture modeling. The ssRBM with tiled-convolution weight sharing (TssRBM) achieves or surpasses the state-of-the-art on texture synthesis and inpainting by parametric models. We also develop a novel RBM model with a spike-and-slab visible layer and binary variables in the hidden layer. This model is designed to be stacked on top of the TssRBM. We show the resulting deep belief network (DBN) is a powerful generative model that improves on single-layer models and is capable of modeling not only single high-resolution and challenging textures but also multiple textures.

Connected through hyperedge HE-1 (induced by source paper "synthesized texture quality assessment via multi scale spatial and statistical texture attributes of image and gradient magnitude coefficients"):
  with members:
    - "texture networks feed forward synthesis of textures and stylized images"
    - "spatial transformer networks"

Connected through hyperedge HE-2 (induced by source paper "plug play generative networks conditional iterative generation of images in latent space"):
  with members:
    - "improving sampling from generative autoencoders with markov chains"
    - "perceptual losses for real time style transfer and super resolution"
    - "neural photo editing with introspective adversarial networks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "multifaceted feature visualization uncovering the different types of features learned by each neuron in deep neural networks"
    - "places an image database for deep scene understanding"

Connected through hyperedge HE-3 (induced by source paper "texture modelling with nested high order markov gibbs random fields"):
  with members:
    - "efficiently inducing features of conditional random fields"

Connected through hyperedge HE-4 (induced by source paper "gsns generative stochastic networks"):
  with members:
    - "deep unsupervised learning using nonequilibrium thermodynamics"
    - "on invariance and selectivity in representation learning"
    - "deep haar scattering networks"
    - "nice non linear independent components estimation"
    - "how transferable are features in deep neural networks"
    - "reweighted wake sleep"
    - "neural variational inference and learning in belief networks"
    - "a semantic matching energy function for learning with multi relational data"

Connected through hyperedge HE-5 (induced by source paper "deep learning of representations looking forward"):
  with members:
    - "a semantic matching energy function for learning with multi relational data"
    - "knowledge matters importance of prior information for optimization"
    - "big neural networks waste capacity"
    - "deep generative stochastic networks trainable by backprop"
    - "generalized denoising auto encoders as generative models"
    - "estimating or propagating gradients through stochastic neurons"
    - "stochastic pooling for regularization of deep convolutional neural networks"
    - "revisiting natural gradient for deep networks"

Connected through hyperedge HE-6 (induced by source paper "deep generative stochastic networks trainable by backprop"):
  with members:
    - "a semantic matching energy function for learning with multi relational data"
    - "generalized denoising auto encoders as generative models"
    - "estimating or propagating gradients through stochastic neurons"
    - "fast gradient based inference with continuous latent variable models in auxiliary form"
    - "a generative process for sampling contractive auto encoders"
    - "what regularized auto encoders learn from the data generating distribution"
    - "sum product networks a new deep architecture"
    - "better mixing via deep representations"

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)