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: "tensorial mixture models"
  Abstract: We introduce a generative model, we call Tensorial Mixture Models (TMMs) based on mixtures of basic#N#component distributions over local structures (e.g. patches in an image) where the dependencies between#N#the local-structures are represented by a "priors tensor" holding the prior probabilities of assigning a#N#component distribution to each local-structure.#N##N#In their general form, TMMs are intractable as the priors tensor is typically of exponential size. However,#N#when the priors tensor is decomposed it gives rise to an arithmetic circuit which in turn transforms the#N#TMM into a Convolutional Arithmetic Circuit (ConvAC). A ConvAC corresponds to a shallow (single hidden layer)#N#network when the priors tensor is decomposed by a CP (sum of rank-1) approach and corresponds to a#N#deep network when the decomposition follows the Hierarchical Tucker (HT) model.#N##N#The ConvAC representation of a TMM possesses several attractive properties. First, the inference is tractable#N#and is implemented by a forward pass through a deep network. Second, the architectural design of the model#N#follows the deep networks community design, i.e.,  the structure of TMMs is determined by just two easily#N#understood factors: size of pooling windows and number of channels. Finally, we demonstrate the effectiveness#N#of our model when tackling the problem of classification with missing data, leveraging TMMs unique ability of#N#tractable marginalization which leads to optimal classifiers regardless of the missingness distribution.

Connected through hyperedge HE-1 (induced by source paper "bayesian learning of sum product networks"):
  with members:
    - "spflow an easy and extensible library for deep probabilistic learning using sum product networks"
    - "optimisation of overparametrized sum product networks"
    - "probabilistic deep learning using random sum product networks"
    - "on the latent variable interpretation in sum product networks"
    - "a unified approach for learning the parameters of sum product networks"
    - "neural architecture search with reinforcement learning"
    - "on the relationship between sum product networks and bayesian networks"
    - "learning bayesian networks the combination of knowledge and statistical data"

Connected through hyperedge HE-2 (induced by source paper "deep convolutional sum product networks for probabilistic image representations"):
  with members:
    - "on the convergence of adam and beyond"
    - "probabilistic deep learning using random sum product networks"
    - "learning graph structured sum product networks for probabilistic semantic maps"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "deep residual learning for image recognition"
    - "multi scale context aggregation by dilated convolutions"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

Connected through hyperedge HE-3 (induced by source paper "tgan deep tensor generative adversarial nets for large image generation"):
  with members:
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved techniques for training gans"
    - "image to image translation with conditional adversarial networks"
    - "a hierarchical latent variable encoder decoder model for generating dialogues"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep generative image models using a laplacian pyramid of adversarial networks"
    - "rethinking the inception architecture for computer vision"

Connected through hyperedge HE-4 (induced by source paper "deep generalized convolutional sum product networks for probabilistic image representations"):
  with members:
    - "learning graph structured sum product networks for probabilistic semantic maps"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "a unified approach for learning the parameters of sum product networks"
    - "adam a method for stochastic optimization"
    - "a differential approach to inference in bayesian networks"
    - "sum product networks a new deep architecture"

Connected through hyperedge HE-5 (induced by source paper "analysis and design of convolutional networks via hierarchical tensor decompositions"):
  with members:
    - "on the expressive power of overlapping architectures of deep learning"
    - "deep learning and quantum entanglement fundamental connections with implications to network design"
    - "boosting dilated convolutional networks with mixed tensor decompositions"
    - "densely connected convolutional networks"
    - "learning functions when is deep better than shallow"
    - "neural machine translation in linear time"
    - "the power of depth for feedforward neural networks"
    - "representation benefits of deep feedforward networks"

Connected through hyperedge HE-6 (induced by source paper "boosting dilated convolutional networks with mixed tensor decompositions"):
  with members:
    - "deep networks with stochastic depth"
    - "training input output recurrent neural networks through spectral methods"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "densely connected convolutional networks"
    - "learning functions when is deep better than shallow"
    - "neural machine translation in linear time"
    - "the power of depth for feedforward neural networks"
    - "representation benefits of deep feedforward networks"

Connected through hyperedge HE-7 (induced by source paper "deep learning and quantum entanglement fundamental connections with implications to network design"):
  with members:
    - "on the expressive power of overlapping architectures of deep learning"
    - "training input output recurrent neural networks through spectral methods"
    - "learning convolutional neural networks for graphs"
    - "deep convolutional networks on graph structured data"
    - "deep residual learning for image recognition"
    - "very deep convolutional networks for large scale image recognition"
    - "spectral networks and locally connected networks on graphs"

Connected through hyperedge HE-8 (induced by source paper "inductive bias of deep convolutional networks through pooling geometry"):
  with members:
    - "learning functions when is deep better than shallow"
    - "the power of depth for feedforward neural networks"
    - "representation benefits of deep feedforward networks"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "simnets a generalization of convolutional networks"

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)