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: "multi view generative adversarial networks"
  Abstract: Learning over multi-view data is a challenging problem with strong practical applications. Most related studies focus on the classification point of view and assume that all the views are available at any time. We consider an extension of this framework in two directions. First, based on the BiGAN model, the Multi-view BiGAN (MV-BiGAN) is able to perform density estimation from multi-view inputs. Second, it can deal with missing views and is able to update its prediction when additional views are provided. We illustrate these properties on a set of experiments over different datasets.

Connected through hyperedge HE-1 (induced by source paper "learning autoencoders with relational regularization"):
  with members:
    - "hierarchical optimal transport for document representation"
    - "scalable gromov wasserstein learning for graph partitioning and matching"
    - "learning generative models across incomparable spaces"
    - "gromov wasserstein factorization models for graph clustering"
    - "gromov wasserstein alignment of word embedding spaces"
    - "sliced wasserstein autoencoder an embarrassingly simple generative model"
    - "the gromov wasserstein distance between networks and stable network invariants"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"

Connected through hyperedge HE-2 (induced by source paper "privacy protection in street view panoramas using depth and multi view imagery"):
  with members:
    - "lidar assisted large scale privacy protection in street view cycloramas"
    - "generative image inpainting with contextual attention"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"

Connected through hyperedge HE-3 (induced by source paper "generative restricted kernel machines"):
  with members:
    - "isolating sources of disentanglement in variational autoencoders"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "automatic goal generation for reinforcement learning agents"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "multi level variational autoencoder learning disentangled representations from grouped observations"
    - "deep variational information bottleneck"
    - "a survey of inductive biases for factorial representation learning"
    - "semantic image inpainting with deep generative models"

Connected through hyperedge HE-4 (induced by source paper "fictitious gan training gans with historical models"):
  with members:
    - "beyond local nash equilibria for adversarial networks"
    - "training generative adversarial networks via primal dual subgradient methods a lagrangian perspective on gan"
    - "dual discriminator generative adversarial nets"
    - "multi agent diverse generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "an online learning approach to generative adversarial networks"
    - "multi generator generative adversarial nets"
    - "gradient descent gan optimization is locally stable"

Connected through hyperedge HE-5 (induced by source paper "acoustic feature learning via deep variational canonical correlation analysis"):
  with members:
    - "deep variational canonical correlation analysis"
    - "representations of language in a model of visually grounded speech signal"
    - "energy based generative adversarial network"
    - "adversarial feature learning"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"
    - "stochastic optimization for deep cca via nonlinear orthogonal iterations"
    - "adam a method for stochastic optimization"
    - "multilingual models for compositional distributed semantics"

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)