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: "autoregressive quantile networks for generative modeling"
  Abstract: We introduce autoregressive implicit quantile networks (AIQN), a fundamentally different approach to generative modeling than those commonly used, that implicitly captures the distribution using quantile regression. AIQN is able to achieve superior perceptual quality and improvements in evaluation metrics, without incurring a loss of sample diversity. The method can be applied to many existing models and architectures. In this work we extend the PixelCNN model with AIQN and demonstrate results on CIFAR-10 and ImageNet using Inception score, FID, non-cherry-picked samples, and inpainting results. We consistently observe that AIQN yields a highly stable algorithm that improves perceptual quality while maintaining a highly diverse distribution.

Connected through hyperedge HE-1 (induced by source paper "augmented normalizing flows bridging the gap between generative flows and latent variable models"):
  with members:
    - "set flow a permutation invariant normalizing flow"
    - "mintnet building invertible neural networks with masked convolutions"
    - "hybrid models with deep and invertible features"
    - "emerging convolutions for generative normalizing flows"
    - "improving exploration in soft actor critic with normalizing flows policies"
    - "leveraging exploration in off policy algorithms via normalizing flows"
    - "stochastic neural network with kronecker flow"
    - "invertible residual networks"

Connected through hyperedge HE-2 (induced by source paper "your gan is secretly an energy based model and you should use discriminator driven latent sampling"):
  with members:
    - "generative modeling by estimating gradients of the data distribution"
    - "logan latent optimisation for generative adversarial networks"
    - "analyzing and improving the image quality of stylegan"
    - "maximum entropy generators for energy based models"
    - "learning implicit generative models with the method of learned moments"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"

Connected through hyperedge HE-3 (induced by source paper "orthogonal convolutional neural networks"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "autogan neural architecture search for generative adversarial networks"
    - "cheap orthogonal constraints in neural networks a simple parametrization of the orthogonal and unitary group"
    - "simple black box adversarial attacks"
    - "superposition of many models into one"
    - "the singular values of convolutional layers"
    - "spectral normalization for generative adversarial networks"
    - "can we gain more from orthogonality regularizations in training deep cnns"

Connected through hyperedge HE-4 (induced by source paper "generative modeling by estimating gradients of the data distribution"):
  with members:
    - "implicit generation and generalization in energy based models"
    - "sliced score matching a scalable approach to density and score estimation"
    - "learning implicit generative models with the method of learned moments"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-5 (induced by source paper "hierarchical autoregressive image models with auxiliary decoders"):
  with members:
    - "preventing posterior collapse with delta vaes"
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "deep contextualized word representations"
    - "large scale gan training for high fidelity natural image synthesis"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "a style based generator architecture for generative adversarial networks"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "representation learning with contrastive predictive coding"

Connected through hyperedge HE-6 (induced by source paper "implicit generation and generalization in energy based models"):
  with members:
    - "lagging inference networks and posterior collapse in variational autoencoders"
    - "maximum entropy generators for energy based models"
    - "the gan landscape losses architectures regularization and normalization"
    - "learning dexterous in hand manipulation"
    - "re evaluating continual learning scenarios a categorization and case for strong baselines"
    - "learning intrinsic image decomposition from watching the world"
    - "spectral normalization for generative adversarial networks"
    - "deep anomaly detection with outlier exposure"

Connected through hyperedge HE-7 (induced by source paper "classification accuracy score for conditional generative models"):
  with members:
    - "training discriminative models to evaluate generative ones"
    - "generating diverse high fidelity images with vq vae 2"
    - "hype a benchmark for human eye perceptual evaluation of generative models"
    - "hierarchical autoregressive image models with auxiliary decoders"
    - "cgans with projection discriminator"
    - "how good is my gan"
    - "geometry score a method for comparing generative adversarial networks"
    - "on accurate evaluation of gans for language generation"

Connected through hyperedge HE-8 (induced by source paper "preventing posterior collapse with delta vaes"):
  with members:
    - "uncertainty in the variational information bottleneck"
    - "stochastic video generation with a learned prior"
    - "spherical latent spaces for stable variational autoencoders"
    - "theory and experiments on vector quantized autoencoders"
    - "associative compression networks for representation learning"
    - "generating sentences by editing prototypes"
    - "attention is all you need"
    - "stochastic variational video prediction"

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)