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: "max margin deep generative models"
  Abstract: Deep generative models (DGMs) are effective on learning multilayered representations of complex data and performing inference of input data by exploring the generative ability. However, little work has been done on examining or empowering the discriminative ability of DGMs on making accurate predictions. This paper presents max-margin deep generative models (mmDGMs), which explore the strongly discriminative principle of max-margin learning to improve the discriminative power of DGMs, while retaining the generative capability. We develop an efficient doubly stochastic subgradient algorithm for the piecewise linear objective. Empirical results on MNIST and SVHN datasets demonstrate that (1) max-margin learning can significantly improve the prediction performance of DGMs and meanwhile retain the generative ability; and (2) mmDGMs are competitive to the state-of-the-art fully discriminative networks by employing deep convolutional neural networks (CNNs) as both recognition and generative models.

Connected through hyperedge HE-1 (induced by source paper "triple generative adversarial networks"):
  with members:
    - "large scale adversarial representation learning"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "dada deep adversarial data augmentation for extremely low data regime classification"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "progressive growing of gans for improved quality stability and variation"
    - "good semi supervised learning that requires a bad gan"
    - "triple generative adversarial nets"

Connected through hyperedge HE-2 (induced by source paper "multi objects generation with amortized structural regularization"):
  with members:
    - "monet unsupervised scene decomposition and representation"
    - "multi object representation learning with iterative variational inference"
    - "sequential attend infer repeat generative modelling of moving objects"
    - "deep structured generative models"
    - "image generation from scene graphs"
    - "graphical generative adversarial networks"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-3 (induced by source paper "semi supervised learning with contrastive predicative coding"):
  with members:
    - "semi supervised learning via compact latent space clustering"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "triple generative adversarial nets"
    - "improved techniques for training gans"
    - "semi supervised learning with ladder networks"
    - "semi supervised learning with deep generative models"
    - "imagenet large scale visual recognition challenge"

Connected through hyperedge HE-4 (induced by source paper "deep generative models with learnable knowledge constraints"):
  with members:
    - "reinforcement learning and control as probabilistic inference tutorial and review"
    - "unsupervised text style transfer using language models as discriminators"
    - "texar a modularized versatile and extensible toolkit for text generation"
    - "learning to write with cooperative discriminators"
    - "unsupervised person image synthesis in arbitrary poses"
    - "training classifiers with natural language explanations"
    - "toward controlled generation of text"
    - "equivalence between policy gradients and soft q learning"

Connected through hyperedge HE-5 (induced by source paper "structured generative adversarial networks"):
  with members:
    - "toward controlled generation of text"
    - "triple generative adversarial nets"
    - "improved training of wasserstein gans"
    - "zm net real time zero shot image manipulation network"
    - "detach and adapt learning cross domain disentangled deep representation"
    - "recurrent topic transition gan for visual paragraph generation"
    - "learning what and where to draw"
    - "temporal ensembling for semi supervised learning"

Connected through hyperedge HE-6 (induced by source paper "max margin deep generative models for semi supervised learning"):
  with members:
    - "improved techniques for training gans"
    - "learning to generate with memory"
    - "theano a python framework for fast computation of mathematical expressions"
    - "on the quantitative analysis of decoder based generative models"
    - "importance weighted autoencoders"
    - "learning deep generative models with doubly stochastic mcmc"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "learning to generate with memory"):
  with members:
    - "importance weighted autoencoders"
    - "end to end memory networks"
    - "learning deep generative models with doubly stochastic mcmc"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "learning to transduce with unbounded memory"
    - "deep generative image models using a laplacian pyramid of adversarial networks"
    - "draw a recurrent neural network for image generation"
    - "semi supervised learning with ladder networks"

Connected through hyperedge HE-8 (induced by source paper "towards better analysis of deep convolutional neural networks"):
  with members:
    - "multifaceted feature visualization uncovering the different types of features learned by each neuron in deep neural networks"
    - "online visual analytics of text streams"
    - "understanding neural networks through deep visualization"
    - "inverting visual representations with convolutional networks"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "on the depth of deep neural networks a theoretical view"

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)