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: "bidirectional helmholtz machines"
  Abstract: Efficient unsupervised training and inference in deep generative models remains a challenging problem. One basic approach, called Helmholtz machine, involves training a top-down directed generative model together with a bottom-up auxiliary model used for approximate inference. Recent results indicate that better generative models can be obtained with better approximate inference procedures. Instead of improving the inference procedure, we here propose a new model which guarantees that the top-down and bottom-up distributions can efficiently invert each other. We achieve this by interpreting both the top-down and the bottom-up directed models as approximate inference distributions and by defining the model distribution to be the geometric mean of these two. We present a lower-bound for the likelihood of this model and we show that optimizing this bound regularizes the model so that the Bhattacharyya distance between the bottom-up and top-down approximate distributions is minimized. This approach results in state of the art generative models which prefer significantly deeper architectures while it allows for orders of magnitude more efficient approximate inference.

Connected through hyperedge HE-1 (induced by source paper "an introduction to variational autoencoders"):
  with members:
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "isolating sources of disentanglement in variational autoencoders"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "bayesian recurrent neural networks"
    - "flow gan combining maximum likelihood and adversarial learning in generative models"
    - "learning discourse level diversity for neural dialog models using conditional variational autoencoders"
    - "voice conversion from unaligned corpora using variational autoencoding wasserstein generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "mim mutual information machine"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "mine mutual information neural estimation"
    - "adversarial symmetric variational autoencoder"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "attention is all you need"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "infovae information maximizing variational autoencoders"

Connected through hyperedge HE-3 (induced by source paper "the thermodynamic variational objective"):
  with members:
    - "implicit reparameterization gradients"
    - "doubly reparameterized gradient estimators for monte carlo objectives"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "filtering variational objectives"
    - "inference compilation and universal probabilistic programming"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "variational inference for monte carlo objectives"

Connected through hyperedge HE-4 (induced by source paper "unrolled generative adversarial networks"):
  with members:
    - "learning to learn by gradient descent by gradient descent"
    - "perceptual losses for real time style transfer and super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "density estimation using real nvp"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "semantic style transfer and turning two bit doodles into fine artworks"
    - "energy based generative adversarial network"
    - "conditional image generation with pixelcnn decoders"

Connected through hyperedge HE-5 (induced by source paper "iterative refinement of approximate posterior for training directed belief networks"):
  with members:
    - "variational inference for monte carlo objectives"
    - "importance weighted autoencoders"
    - "muprop unbiased backpropagation for stochastic neural networks"
    - "neural adaptive sequential monte carlo"
    - "adam a method for stochastic optimization"
    - "reweighted wake sleep"
    - "neural variational inference and learning in belief networks"
    - "estimating or propagating gradients through stochastic neurons for conditional computation"

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)