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: "deep variational canonical correlation analysis"
  Abstract: We present deep variational canonical correlation analysis (VCCA), a deep multi-view learning model that extends the latent variable model interpretation of linear CCA~\citep{BachJordan05a} to nonlinear observation models parameterized by deep neural networks (DNNs). Computing the marginal data likelihood, as well as inference of the latent variables, are intractable under this model. We derive a variational lower bound of the data likelihood by parameterizing the posterior density of the latent variables with another DNN, and approximate the lower bound via Monte Carlo sampling. Interestingly, the resulting model resembles that of multi-view autoencoders~\citep{Ngiam_11b}, with the key distinction of an additional sampling procedure at the bottleneck layer. We also propose a variant of VCCA called VCCA-private which can, in addition to the ``common variables'' underlying both views, extract the ``private variables'' within each view. We demonstrate that VCCA-private is able to disentangle the shared and private information for multi-view data without hard supervision.

Connected through hyperedge HE-1 (induced by source paper "learning robust representations via multi view information bottleneck"):
  with members:
    - "semantically tied paired cycle consistency for zero shot sketch based image retrieval"
    - "on mutual information maximization for representation learning"
    - "contrastive multiview coding"
    - "on variational bounds of mutual information"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "mine mutual information neural estimation"
    - "representation learning with contrastive predictive coding"
    - "emergence of invariance and disentanglement in deep representations"

Connected through hyperedge HE-2 (induced by source paper "variational inference for deep probabilistic canonical correlation analysis"):
  with members:
    - "an introduction to variational autoencoders"
    - "deep multimodal subspace clustering networks"
    - "deep subspace clustering networks"
    - "acoustic feature learning via deep variational canonical correlation analysis"
    - "adam a method for stochastic optimization"
    - "manifold relevance determination"
    - "a tutorial on spectral clustering"
    - "objective criteria for the evaluation of clustering methods"

Connected through hyperedge HE-3 (induced by source paper "multimodal generative models for compositional representation learning"):
  with members:
    - "factorized inference in deep markov models for incomplete multimodal time series"
    - "measuring compositionality in representation learning"
    - "vae gans for probabilistic compressive image recovery uncertainty analysis"
    - "shapeglot learning language for shape differentiation"
    - "neural autoregressive flows"
    - "graph r cnn for scene graph generation"
    - "yolov3 an incremental improvement"
    - "multimodal matching using a hybrid convolutional neural network"

Connected through hyperedge HE-4 (induced by source paper "a hierarchical grocery store image dataset with visual and semantic labels"):
  with members:
    - "advances in variational inference"
    - "generative models of visually grounded imagination"
    - "fruit recognition from images using deep learning"
    - "densely connected convolutional networks"
    - "inter battery topic representation learning"
    - "deep fruit detection in orchards"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep metric learning via lifted structured feature embedding"

Connected through hyperedge HE-5 (induced by source paper "a generative map for image based camera localization"):
  with members:
    - "variational autoencoded regression high dimensional regression of visual data on complex manifold"
    - "learning models for visual 3d localization with implicit mapping"
    - "vidloc a deep spatio temporal model for 6 dof video clip relocalization"
    - "geometric loss functions for camera pose regression with deep learning"
    - "image based localization using hourglass networks"
    - "geometry aware learning of maps for camera localization"
    - "generative models of visually grounded imagination"
    - "image based localization using lstms for structured feature correlation"

Connected through hyperedge HE-6 (induced by source paper "disentangled cumulants help successor representations transfer to new tasks"):
  with members:
    - "transfer in deep reinforcement learning using successor features and generalised policy improvement"
    - "regularized hierarchical policies for compositional transfer in robotics"
    - "fast task inference with variational intrinsic successor features"
    - "visual reinforcement learning with imagined goals"
    - "diversity is all you need learning skills without a reward function"
    - "curiosity driven exploration of learned disentangled goal spaces"
    - "towards a definition of disentangled representations"
    - "eigenoption discovery through the deep successor representation"

Connected through hyperedge HE-7 (induced by source paper "wyner vae joint and conditional generation with succinct common representation learning"):
  with members:
    - "adversarial symmetric variational autoencoder"
    - "information theoretic analysis of generalization capability of learning algorithms"
    - "generative models of visually grounded imagination"
    - "deep variational information bottleneck"
    - "adam a method for stochastic optimization"
    - "semi supervised learning with deep generative models"
    - "deep learning face attributes in the wild"
    - "distributed channel synthesis"

Connected through hyperedge HE-8 (induced by source paper "shared generative latent representation learning for multi view clustering"):
  with members:
    - "deep clustering for unsupervised learning of visual features"
    - "deep subspace clustering networks"
    - "sharing deep generative representation for perceived image reconstruction from human brain activity"
    - "variational deep embedding an unsupervised and generative approach to clustering"
    - "towards k means friendly spaces simultaneous deep learning and clustering"
    - "unsupervised deep embedding for clustering analysis"
    - "adam a method for stochastic optimization"
    - "a survey on multi view learning"

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)