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: "draw a recurrent neural network for image generation"
  Abstract: This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural network architecture for image generation. DRAW networks combine a novel spatial attention mechanism that mimics the foveation of the human eye, with a sequential variational auto-encoding framework that allows for the iterative construction of complex images. The system substantially improves on the state of the art for generative models on MNIST, and, when trained on the Street View House Numbers dataset, it generates images that cannot be distinguished from real data with the naked eye.

Connected through hyperedge HE-1 (induced by source paper "twostreamvan improving motion modeling in video generation"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "stochastic adversarial video prediction"
    - "probabilistic video generation using holistic attribute control"
    - "stochastic video generation with a learned prior"
    - "a style based generator architecture for generative adversarial networks"
    - "on convergence and stability of gans"
    - "stochastic variational video prediction"
    - "unsupervised learning of disentangled representations from video"

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 "ganwriting content conditioned generation of styled handwritten word images"):
  with members:
    - "adversarial generation of handwritten text images conditioned on sequences"
    - "few shot unsupervised image to image translation"
    - "unsupervised writer adaptation for synthetic to real handwritten word recognition"
    - "pros and cons of gan evaluation measures"
    - "multimodal unsupervised image to image translation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "generating handwritten chinese characters using cyclegan"

Connected through hyperedge HE-4 (induced by source paper "deep automodulators"):
  with members:
    - "image2stylegan how to embed images into the stylegan latent space"
    - "implicit autoencoders"
    - "analyzing and improving the image quality of stylegan"
    - "large scale adversarial representation learning"
    - "generating diverse high fidelity images with vq vae 2"
    - "towards photographic image manipulation with balanced growing of generative autoencoders"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "pioneer networks progressively growing generative autoencoder"

Connected through hyperedge HE-5 (induced by source paper "double backpropagation for training autoencoders against adversarial attack"):
  with members:
    - "image super resolution as a defense against adversarial attacks"
    - "when does label smoothing help"
    - "adversarial attacks on variational autoencoders"
    - "sparse dnns with improved adversarial robustness"
    - "comdefend an efficient image compression model to defend adversarial examples"
    - "the history began from alexnet a comprehensive survey on deep learning approaches"
    - "deflecting adversarial attacks with pixel deflection"
    - "deep defense training dnns with improved adversarial robustness"

Connected through hyperedge HE-6 (induced by source paper "active perception and representation for robotic manipulation"):
  with members:
    - "saccader improving accuracy of hard attention models for vision"
    - "model based reinforcement learning for atari"
    - "deep reinforcement learning for vision based robotic grasping a simulated comparative evaluation of off policy methods"
    - "learning actionable representations from visual observations"
    - "visual reinforcement learning with imagined goals"
    - "visual foresight model based deep reinforcement learning for vision based robotic control"
    - "soft actor critic algorithms and applications"
    - "soft actor critic off policy maximum entropy deep reinforcement learning with a stochastic actor"

Connected through hyperedge HE-7 (induced by source paper "vq draw a sequential discrete vae"):
  with members:
    - "a simple framework for contrastive learning of visual representations"
    - "generating diverse high fidelity images with vq vae 2"
    - "group normalization"
    - "representation learning with contrastive predictive coding"
    - "progressive growing of gans for improved quality stability and variation"
    - "neural discrete representation learning"
    - "the concrete distribution a continuous relaxation of discrete random variables"
    - "variational inference for monte carlo objectives"

Connected through hyperedge HE-8 (induced by source paper "progressive learning and disentanglement of hierarchical representations"):
  with members:
    - "improving disentangled representation learning with the beta bernoulli process"
    - "isolating sources of disentanglement in variational autoencoders"
    - "a fully progressive approach to single image super resolution"
    - "progressive growing of gans for improved quality stability and variation"
    - "learning hierarchical features from generative models"
    - "stackgan realistic image synthesis with stacked generative adversarial networks"
    - "an architecture for deep hierarchical generative models"
    - "learning to generate with memory"

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)