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 for semi supervised learning"
  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, it is relatively insufficient to empower the discriminative ability of DGMs on making accurate predictions. This paper presents max-margin deep generative models (mmDGMs) and a class-conditional variant (mmDCGMs), which explore the strongly discriminative principle of max-margin learning to improve the predictive performance of DGMs in both supervised and semi-supervised learning, while retaining the generative capability. In semi-supervised learning, we use the predictions of a max-margin classifier as the missing labels instead of performing full posterior inference for efficiency; we also introduce additional max-margin and label-balance regularization terms of unlabeled data for effectiveness. We develop an efficient doubly stochastic subgradient algorithm for the piecewise linear objectives in different settings. Empirical results on various datasets demonstrate that: (1) max-margin learning can significantly improve the prediction performance of DGMs and meanwhile retain the generative ability; (2) in supervised learning, mmDGMs are competitive to the best fully discriminative networks when employing convolutional neural networks as the generative and recognition models; and (3) in semi-supervised learning, mmDCGMs can perform efficient inference and achieve state-of-the-art classification results on several benchmarks.

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 "boosting generative models by leveraging cascaded meta models"):
  with members:
    - "boosted generative models"
    - "importance weighted autoencoders"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-3 (induced by source paper "adversarial constraint learning for structured prediction"):
  with members:
    - "improved training of wasserstein gans"
    - "a semantic loss function for deep learning with symbolic knowledge"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "improved techniques for training gans"
    - "label free supervision of neural networks with physics and domain knowledge"
    - "weakly and semi supervised learning of a dcnn for semantic image segmentation"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "constrained convolutional neural networks for weakly supervised segmentation"

Connected through hyperedge HE-4 (induced by source paper "semi supervised learning via compact latent space clustering"):
  with members:
    - "good semi supervised learning that requires a bad gan"
    - "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"
    - "diffusion convolutional neural networks"
    - "semi supervised learning with ladder networks"
    - "semi supervised learning with deep generative models"

Connected through hyperedge HE-5 (induced by source paper "learning to write stylized chinese characters by reading a handful of examples"):
  with members:
    - "building fast and compact convolutional neural networks for offline handwritten chinese character recognition"
    - "learning hierarchical features from generative models"
    - "multi level variational autoencoder learning disentangled representations from grouped observations"
    - "disentangling factors of variation in deep representations using adversarial training"
    - "online and offline handwritten chinese character recognition a comprehensive study and new benchmark"
    - "densely connected convolutional networks"
    - "adversarial feature learning"
    - "learning typographic style"

Connected through hyperedge HE-6 (induced by source paper "triple generative adversarial nets"):
  with members:
    - "temporal ensembling for semi supervised learning"
    - "improved techniques for training gans"
    - "adversarial feature learning"
    - "weakly supervised disentangling with recurrent transformations for 3d view synthesis"
    - "infogan interpretable representation learning by information maximizing generative adversarial nets"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "generative moment matching 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.CV(Computer Vision and Pattern Recognition)