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: "implicit maximum likelihood estimation"
  Abstract: Implicit probabilistic models are models defined naturally in terms of a sampling procedure and often induces a likelihood function that cannot be expressed explicitly. We develop a simple method for estimating parameters in implicit models that does not require knowledge of the form of the likelihood function or any derived quantities, but can be shown to be equivalent to maximizing likelihood under some conditions. Our result holds in the non-asymptotic parametric setting, where both the capacity of the model and the number of data examples are finite. We also demonstrate encouraging experimental results.

Connected through hyperedge HE-1 (induced by source paper "learning generative models using denoising density estimators"):
  with members:
    - "generative modeling by estimating gradients of the data distribution"
    - "a style based generator architecture for generative adversarial networks"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "deep mean shift priors for image restoration"
    - "progressive growing of gans for improved quality stability and variation"
    - "searching for activation functions"
    - "improved training of wasserstein gans"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"

Connected through hyperedge HE-2 (induced by source paper "batch norm with entropic regularization turns deterministic autoencoders into generative models"):
  with members:
    - "online normalization for training neural networks"
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "diagnosing and enhancing vae models"
    - "from variational to deterministic autoencoders"
    - "batch normalization is a cause of adversarial vulnerability"
    - "fixup initialization residual learning without normalization"
    - "diverse image synthesis from semantic layouts via conditional imle"
    - "batch renormalization towards reducing minibatch dependence in batch normalized models"

Connected through hyperedge HE-3 (induced by source paper "deep learning for single image super resolution a brief review"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "fast accurate and lightweight super resolution with cascading residual network"
    - "fast and accurate single image super resolution via information distillation network"
    - "super resolution based on image adapted cnn denoisers incorporating generalization of training data and internal learning in test time"
    - "unsupervised image super resolution using cycle in cycle generative adversarial networks"
    - "the perception distortion tradeoff"
    - "super resolution via conditional implicit maximum likelihood estimation"
    - "residual dense network for image super resolution"

Connected through hyperedge HE-4 (induced by source paper "generative latent flow"):
  with members:
    - "diagnosing and enhancing vae models"
    - "spectral normalization for generative adversarial networks"
    - "isolating sources of disentanglement in variational autoencoders"
    - "large scale gan training for high fidelity natural image synthesis"
    - "progressive growing of gans for improved quality stability and variation"
    - "learnable explicit density for continuous latent space and variational inference"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "vae with a vampprior"

Connected through hyperedge HE-5 (induced by source paper "learning implicit generative models by matching perceptual features"):
  with members:
    - "on the convergence of adam and beyond"
    - "learning implicit generative models with the method of learned moments"
    - "deep clustering for unsupervised learning of visual features"
    - "spectral normalization for generative adversarial networks"
    - "mcgan mean and covariance feature matching gan"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "mmd gan towards deeper understanding of moment matching network"
    - "improved training of wasserstein gans"

Connected through hyperedge HE-6 (induced by source paper "generative latent flow a framework for non adversarial image generation"):
  with members:
    - "diagnosing and enhancing vae models"
    - "on the convergence of adam and beyond"
    - "large scale gan training for high fidelity natural image synthesis"
    - "non adversarial image synthesis with generative latent nearest neighbors"
    - "style transfer from non parallel text by cross alignment"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "adversarial variational bayes unifying variational autoencoders and generative adversarial networks"
    - "on unifying deep generative models"

Connected through hyperedge HE-7 (induced by source paper "training generative networks using random discriminators"):
  with members:
    - "on the convergence and robustness of training gans with regularized optimal transport"
    - "on the behavior of the expectation maximization algorithm for mixture models"
    - "on the implicit assumptions of gans"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "improved training of wasserstein gans"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "energy based generative adversarial network"
    - "least squares generative adversarial networks"

Connected through hyperedge HE-8 (induced by source paper "adaptive estimation for approximate k nearest neighbor computations"):
  with members:
    - "approximate ranking from pairwise comparisons"
    - "adaptive monte carlo optimization"
    - "active ranking from pairwise comparisons and when parametric assumptions don t help"
    - "hyperband a novel bandit based approach to hyperparameter optimization"

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)