human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "improving variational inference with inverse autoregressive flow":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gradient boosted flows")
  Member "improved techniques for training gans":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "unsupervised representation learning with deep convolutional generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "on the expressive power of deep learning a tensor analysis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "tensor networks for language modeling")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "nice non linear independent components estimation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "blur noise and compression robust generative adversarial networks")
  Member "semi supervised learning with deep generative models":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "ha ccn hierarchical attention based crowd counting network")
  Member "caffe convolutional architecture for fast feature embedding":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")

Question: Please tell me which class the source paper 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)