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: "parallel multiscale autoregressive density estimation"
  Abstract: PixelCNN achieves state-of-the-art results in density estimation for natural images. Although training is fast, inference is costly, requiring one network evaluation per pixel; O(N) for N pixels. This can be sped up by caching activations, but still involves generating each pixel sequentially. In this work, we propose a parallelized PixelCNN that allows more efficient inference by modeling certain pixel groups as conditionally independent. Our new PixelCNN model achieves competitive density estimation and orders of magnitude speedup - O(log N) sampling instead of O(N) - enabling the practical generation of 512x512 images. We evaluate the model on class-conditional image generation, text-to-image synthesis, and action-conditional video generation, showing that our model achieves the best results among non-pixel-autoregressive density models that allow efficient sampling.

Connected through hyperedge HE-1 (induced by source paper "videoflow a flow based generative model for video"):
  with members:
    - "tensor2tensor for neural machine translation"
    - "stochastic adversarial video prediction"
    - "stochastic video generation with a learned prior"
    - "fast generation for convolutional autoregressive models"
    - "self supervised visual planning with temporal skip connections"
    - "the reversible residual network backpropagation without storing activations"
    - "attention is all you need"
    - "stochastic variational video prediction"

Connected through hyperedge HE-2 (induced by source paper "generating long sequences with sparse transformers"):
  with members:
    - "the challenge of realistic music generation modelling raw audio at scale"
    - "generating wikipedia by summarizing long sequences"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "character level language modeling with deeper self attention"
    - "music transformer"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "monotonic chunkwise attention"
    - "attention is all you need"

Connected through hyperedge HE-3 (induced by source paper "face hallucination by attentive sequence optimization with reinforcement learning"):
  with members:
    - "cross modal attentional context learning for rgb d object detection"
    - "facial landmark machines a backbone branches architecture with progressive representation learning"
    - "fsrnet end to end learning face super resolution with facial priors"
    - "multi label image recognition by recurrently discovering attentional regions"
    - "attention aware face hallucination via deep reinforcement learning"
    - "recurrent attentional reinforcement learning for multi label image recognition"
    - "zero shot super resolution using deep internal learning"
    - "learning to hallucinate face images via component generation and enhancement"

Connected through hyperedge HE-4 (induced by source paper "image generation from layout"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "chatpainter improving text to image generation using dialogue"
    - "text2scene generating compositional scenes from textual descriptions"
    - "modular generative adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "image generation from scene graphs"
    - "drit diverse image to image translation via disentangled representations"

Connected through hyperedge HE-5 (induced by source paper "visual foresight model based deep reinforcement learning for vision based robotic control"):
  with members:
    - "learning plannable representations with causal infogan"
    - "world models"
    - "stochastic adversarial video prediction"
    - "learning synergies between pushing and grasping with self supervised deep reinforcement learning"
    - "few shot goal inference for visuomotor learning and planning"
    - "solar deep structured latent representations for model based reinforcement learning"
    - "self supervised visual planning with temporal skip connections"
    - "combining self supervised learning and imitation for vision based rope manipulation"

Connected through hyperedge HE-6 (induced by source paper "probabilistic semantic inpainting with pixel constrained cnns"):
  with members:
    - "attentive neural processes"
    - "image inpainting for irregular holes using partial convolutions"
    - "neural processes"
    - "free form image inpainting with gated convolution"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "the cramer distance as a solution to biased wasserstein gradients"
    - "generative image inpainting with contextual attention"
    - "fast generation for convolutional autoregressive models"

Connected through hyperedge HE-7 (induced by source paper "theory and experiments on vector quantized autoencoders"):
  with members:
    - "deterministic non autoregressive neural sequence modeling by iterative refinement"
    - "discrete autoencoders for sequence models"
    - "fast decoding in sequence models using discrete latent variables"
    - "backpropagation through the void optimizing control variates for black box gradient estimation"
    - "attention is all you need"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "regularizing neural networks by penalizing confident output distributions"
    - "sequence level knowledge distillation"

Connected through hyperedge HE-8 (induced by source paper "pixel deconvolutional networks"):
  with members:
    - "conditional image generation with pixelcnn decoders"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "generative adversarial text to image synthesis"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "u net convolutional networks for biomedical image segmentation"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "made masked autoencoder for distribution estimation"
    - "deep residual learning for image recognition"

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.CV(Computer Vision and Pattern Recognition)