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: "made masked autoencoder for distribution estimation"
  Abstract: There has been a lot of recent interest in designing neural network models to estimate a distribution from a set of examples. We introduce a simple modification for autoencoder neural networks that yields powerful generative models. Our method masks the autoencoder's parameters to respect autoregressive constraints: each input is reconstructed only from previous inputs in a given ordering. Constrained this way, the autoencoder outputs can be interpreted as a set of conditional probabilities, and their product, the full joint probability. We can also train a single network that can decompose the joint probability in multiple different orderings. Our simple framework can be applied to multiple architectures, including deep ones. Vectorized implementations, such as on GPUs, are simple and fast. Experiments demonstrate that this approach is competitive with state-of-the-art tractable distribution estimators. At test time, the method is significantly faster and scales better than other autoregressive estimators.

Connected through hyperedge HE-1 (induced by source paper "polygen an autoregressive generative model of 3d meshes"):
  with members:
    - "pointflow 3d point cloud generation with continuous normalizing flows"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "the curious case of neural text degeneration"
    - "stabilizing transformers for reinforcement learning"
    - "generating long sequences with sparse transformers"
    - "image transformer"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"

Connected through hyperedge HE-2 (induced by source paper "training deep energy based models with f divergence minimization"):
  with members:
    - "exponential family estimation via adversarial dynamics embedding"
    - "maximum entropy generators for energy based models"
    - "kernel exponential family estimation via doubly dual embedding"
    - "spectral normalization for generative adversarial networks"
    - "which training methods for gans do actually converge"
    - "improved training of wasserstein gans"
    - "gradient descent gan optimization is locally stable"
    - "seqgan sequence generative adversarial nets with policy gradient"

Connected through hyperedge HE-3 (induced by source paper "graphaf a flow based autoregressive model for molecular graph generation"):
  with members:
    - "graph normalizing flows"
    - "molecularrnn generating realistic molecular graphs with optimized properties"
    - "graph convolutional policy network for goal directed molecular graph generation"
    - "constrained generation of semantically valid graphs via regularizing variational autoencoders"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graphrnn generating realistic graphs with deep auto regressive models"
    - "automatic chemical design using a data driven continuous representation of molecules"
    - "molecular sets moses a benchmarking platform for molecular generation models"

Connected through hyperedge HE-4 (induced by source paper "woodbury transformations for deep generative flows"):
  with members:
    - "integer discrete flows and lossless compression"
    - "discrete flows invertible generative models of discrete data"
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "emerging convolutions for generative normalizing flows"
    - "neural autoregressive flows"
    - "neural ordinary differential equations"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-5 (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"
    - "implicit maximum likelihood estimation"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "nonlinear equation solving a faster alternative to feedforward computation"):
  with members:
    - "pytorch an imperative style high performance deep learning library"
    - "graph partition neural networks for semi supervised classification"
    - "fast generation for convolutional autoregressive models"
    - "attention is all you need"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "parallel wavenet fast high fidelity speech synthesis"
    - "fashion mnist a novel image dataset for benchmarking machine learning algorithms"
    - "conditional image generation with pixelcnn decoders"

Connected through hyperedge HE-7 (induced by source paper "gradient boosted flows"):
  with members:
    - "inference suboptimality in variational autoencoders"
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "gaussian process prior variational autoencoders"
    - "improving explorability in variational inference with annealed variational objectives"
    - "vae with a vampprior"
    - "improving variational auto encoders using householder flow"
    - "improving variational inference with inverse autoregressive flow"
    - "density estimation using real nvp"

Connected through hyperedge HE-8 (induced by source paper "granular learning with deep generative models using highly contaminated data"):
  with members:
    - "deep generative models strike back improving understanding and evaluation in light of unmet expectations for ood data"
    - "revisiting self supervised visual representation learning"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "deep anomaly detection with outlier exposure"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "does your model know the digit 6 is not a cat a less biased evaluation of outlier detectors"
    - "density estimation using real nvp"
    - "identity mappings in deep residual networks"

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)