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: "dvae discrete variational autoencoders with overlapping transformations"
  Abstract: Training of discrete latent variable models remains challenging because passing gradient information through discrete units is difficult. We propose a new class of smoothing transformations based on a mixture of two overlapping distributions, and show that the proposed transformation can be used for training binary latent models with either directed or undirected priors. We derive a new variational bound to efficiently train with Boltzmann machine priors. Using this bound, we develop DVAE++, a generative model with a global discrete prior and a hierarchy of convolutional continuous variables. Experiments on several benchmarks show that overlapping transformations outperform other recent continuous relaxations of discrete latent variables including Gumbel-Softmax (Maddison et al., 2016; Jang et al., 2016), and discrete variational autoencoders (Rolfe 2016).

Connected through hyperedge HE-1 (induced by source paper "pixelvae improved pixelvae with discrete prior"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "gumbolt extending gumbel trick to boltzmann priors"
    - "deep visual domain adaptation a survey"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "prototypical networks for few shot learning"
    - "fixing a broken elbo"
    - "vae with a vampprior"
    - "pixelsnail an improved autoregressive generative model"

Connected through hyperedge HE-2 (induced by source paper "neural symbolic descriptive action model from images the search for strips"):
  with members:
    - "unsupervised grounding of plannable first order logic representation from images"
    - "learning action models from disordered and noisy plan traces"
    - "towards stable symbol grounding with zero suppressed state autoencoder"
    - "learning strips action models with classical planning"
    - "learning plannable representations with causal infogan"
    - "learning finite state representations of recurrent policy networks"
    - "extracting action sequences from texts based on deep reinforcement learning"
    - "lstm based goal recognition in latent space"

Connected through hyperedge HE-3 (induced by source paper "direct optimization through arg max for discrete variational auto encoder"):
  with members:
    - "nash toward end to end neural architecture for generative semantic hashing"
    - "differentiable perturb and parse semi supervised parsing with a structured variational autoencoder"
    - "structvae tree structured latent variable models for semi supervised semantic parsing"
    - "toward controlled generation of text"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "learning hard alignments with variational inference"
    - "emergence of grounded compositional language in multi agent populations"

Connected through hyperedge HE-4 (induced by source paper "gumbolt extending gumbel trick to boltzmann priors"):
  with members:
    - "overcoming catastrophic forgetting with hard attention to the task"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "rebar low variance unbiased gradient estimates for discrete latent variable models"
    - "tackling over pruning in variational autoencoders"
    - "pixelvae a latent variable model for natural images"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "variational inference for monte carlo objectives"
    - "pixel recurrent neural networks"

Connected through hyperedge HE-5 (induced by source paper "handling incomplete heterogeneous data using vaes"):
  with members:
    - "autoencoders and probabilistic inference with missing data an exact solution for the factor analysis case"
    - "deep unsupervised clustering with gaussian mixture variational autoencoders"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "improved variational autoencoders for text modeling using dilated convolutions"
    - "unsupervised image to image translation networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "do gans actually learn the distribution an empirical study"

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)