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: "boosted generative models"
  Abstract: We propose a novel approach for using unsupervised boosting to create an ensemble of generative models, where models are trained in sequence to correct earlier mistakes. Our meta-algorithmic framework can leverage any existing base learner that permits likelihood evaluation, including recent deep expressive models. Further, our approach allows the ensemble to include discriminative models trained to distinguish real data from model-generated data. We show theoretical conditions under which incorporating a new model in the ensemble will improve the fit and empirically demonstrate the effectiveness of our black-box boosting algorithms on density estimation, classification, and sample generation on benchmark datasets for a wide range of generative models.

Connected through hyperedge HE-1 (induced by source paper "fair generative modeling via weak supervision"):
  with members:
    - "towards gan benchmarks which require generalization"
    - "scalable fair clustering"
    - "counterpoint by convolution"
    - "fair clustering through fairlets"
    - "quantitatively evaluating gans with divergences proposed for training"
    - "datasheets for datasets"
    - "what is the effect of importance weighting in deep learning"
    - "fair coresets and streaming algorithms for fair k means clustering"

Connected through hyperedge HE-2 (induced by source paper "understanding the limitations of variational mutual information estimators"):
  with members:
    - "on mutual information maximization for representation learning"
    - "on variational bounds of mutual information"
    - "ccmi classifier based conditional mutual information estimation"
    - "wasserstein dependency measure for representation learning"
    - "formal limitations on the measurement of mutual information"
    - "towards a definition of disentangled representations"
    - "mine mutual information neural estimation"
    - "bias and generalization in deep generative models an empirical study"

Connected through hyperedge HE-3 (induced by source paper "bridging the gap between f gans and wasserstein gans"):
  with members:
    - "generative modeling by estimating gradients of the data distribution"
    - "learning implicit generative models with the method of learned moments"
    - "breaking the curse of horizon infinite horizon off policy estimation"
    - "a convex duality framework for gans"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the inductive bias of restricted f gans"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-4 (induced by source paper "neural network based explicit mixture models and expectation maximization based learning"):
  with members:
    - "improved training of generative adversarial networks using representative features"
    - "disconnected manifold learning for generative adversarial networks"
    - "improving gans using optimal transport"
    - "an empirical study on evaluation metrics of generative adversarial networks"
    - "multi agent diverse generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "density estimation using real nvp"

Connected through hyperedge HE-5 (induced by source paper "boosting generative models by leveraging cascaded meta models"):
  with members:
    - "max margin deep generative models for semi supervised learning"
    - "importance weighted autoencoders"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-6 (induced by source paper "learning near optimal convex combinations of basis models with generalization guarantees"):
  with members:
    - "adam a method for stochastic optimization"
    - "breaking the curse of dimensionality with convex neural networks"
    - "on the doubt about margin explanation of boosting"

Connected through hyperedge HE-7 (induced by source paper "dropout gan learning from a dynamic ensemble of discriminators"):
  with members:
    - "stabilizing gan training with multiple random projections"
    - "on convergence and stability of gans"
    - "dual discriminator generative adversarial nets"
    - "multi agent diverse generative adversarial networks"
    - "cm gans cross modal generative adversarial networks for common representation learning"
    - "triangle generative adversarial networks"
    - "the numerics of gans"
    - "multi generator generative adversarial nets"

Connected through hyperedge HE-8 (induced by source paper "boosted density estimation remastered"):
  with members:
    - "boosting variational inference an optimization perspective"
    - "f gans in an information geometric nutshell"

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)