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: "pixelsnail an improved autoregressive generative model"
  Abstract: Autoregressive generative models consistently achieve the best results in density estimation tasks involving high dimensional data, such as images or audio. They pose density estimation as a sequence modeling task, where a recurrent neural network (RNN) models the conditional distribution over the next element conditioned on all previous elements. In this paradigm, the bottleneck is the extent to which the RNN can model long-range dependencies, and the most successful approaches rely on causal convolutions, which offer better access to earlier parts of the sequence than conventional RNNs. Taking inspiration from recent work in meta reinforcement learning, where dealing with long-range dependencies is also essential, we introduce a new generative model architecture that combines causal convolutions with self attention. In this note, we describe the resulting model and present state-of-the-art log-likelihood results on CIFAR-10 (2.85 bits per dim) and $32 \times 32$ ImageNet (3.80 bits per dim). Our implementation is available at this https URL

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 "efficient content based sparse attention with routing transformers"):
  with members:
    - "multi task deep neural networks for natural language understanding"
    - "transformer xl attentive language models beyond a fixed length context"
    - "generating long sequences with sparse transformers"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "adaptive attention span in transformers"
    - "generating wikipedia by summarizing long sequences"
    - "self attention with relative position representations"
    - "an analysis of neural language modeling at multiple scales"

Connected through hyperedge HE-3 (induced by source paper "closing the dequantization gap pixelcnn as a single layer flow"):
  with members:
    - "learning discrete distributions by dequantization"
    - "integer discrete flows and lossless compression"
    - "sum of squares polynomial flow"
    - "discrete flows invertible generative models of discrete data"
    - "generating long sequences with sparse transformers"
    - "image transformer"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "ffjord free form continuous dynamics for scalable reversible generative models"

Connected through hyperedge HE-4 (induced by source paper "deep learning based video coding a review and a case study"):
  with members:
    - "learning for video compression"
    - "image and video compression with neural networks a review"
    - "enhancing quality for hevc compressed videos"
    - "on the classification distortion perception tradeoff"
    - "learning based facial image compression with semantic fidelity metric"
    - "joint autoregressive and hierarchical priors for learned image compression"
    - "learned scalable image compression with bidirectional context disentanglement network"
    - "enlarging context with low cost efficient arithmetic coding with trimmed convolution"

Connected through hyperedge HE-5 (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"
    - "dvae discrete variational autoencoders with overlapping transformations"
    - "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"

Connected through hyperedge HE-6 (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"
    - "autoregressive quantile networks for generative modeling"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "scaling autoregressive video models"):
  with members:
    - "model based reinforcement learning for atari"
    - "hierarchical autoregressive image models with auxiliary decoders"
    - "generating long sequences with sparse transformers"
    - "image transformer"
    - "a short note about kinetics 600"
    - "blockwise parallel decoding for deep autoregressive models"
    - "stochastic video generation with a learned prior"
    - "towards accurate generative models of video a new metric challenges"

Connected through hyperedge HE-8 (induced by source paper "dimensionality reduction flows"):
  with members:
    - "flow improving flow based generative models with variational dequantization and architecture design"
    - "videoflow a flow based generative model for video"
    - "semi supervised learning with normalizing flows"
    - "emerging convolutions for generative normalizing flows"
    - "gansynth adversarial neural audio synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "neural discrete representation learning"

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)