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: "deep feature consistent variational autoencoder"
  Abstract: We present a novel method for constructing Variational Autoencoder (VAE). Instead of using pixel-by-pixel loss, we enforce deep feature consistency between the input and the output of a VAE, which ensures the VAE's output to preserve the spatial correlation characteristics of the input, thus leading the output to have a more natural visual appearance and better perceptual quality. Based on recent deep learning works such as style transfer, we employ a pre-trained deep convolutional neural network (CNN) and use its hidden features to define a feature perceptual loss for VAE training. Evaluated on the CelebA face dataset, we show that our model produces better results than other methods in the literature. We also show that our method can produce latent vectors that can capture the semantic information of face expressions and can be used to achieve state-of-the-art performance in facial attribute prediction.

Connected through hyperedge HE-1 (induced by source paper "adaptive density estimation for generative models"):
  with members:
    - "improving generalization and stability of generative adversarial networks"
    - "cgans with projection discriminator"
    - "how good is my gan"
    - "spectral normalization for generative adversarial networks"
    - "improving gans using optimal transport"
    - "large scale gan training for high fidelity natural image synthesis"
    - "generating high fidelity images with subscale pixel networks and multidimensional upscaling"
    - "fast generation for convolutional autoregressive models"

Connected through hyperedge HE-2 (induced by source paper "towards a controllable disentanglement network"):
  with members:
    - "soft gated warping gan for pose guided person image synthesis"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "isolating sources of disentanglement in variational autoencoders"
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "latent constraints learning to generate conditionally from unconditional generative models"
    - "improved training of wasserstein gans"
    - "semantically decomposing the latent spaces of generative adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "improving variational autoencoder with deep feature consistent and generative adversarial training"):
  with members:
    - "local deep feature alignment for unsupervised dimension reduction"
    - "style aggregated network for facial landmark detection"
    - "graph autoencoder based unsupervised feature selection with broad and local data structure preservation"
    - "crossing nets combining gans and vaes with a shared latent space for hand pose estimation"
    - "loss sensitive generative adversarial networks on lipschitz densities"
    - "progressive growing of gans for improved quality stability and variation"
    - "fast and scalable distributed deep convolutional autoencoder for fmri big data analytics"
    - "on unifying deep generative models"

Connected through hyperedge HE-4 (induced by source paper "generative latent flow"):
  with members:
    - "diagnosing and enhancing vae models"
    - "spectral normalization for generative adversarial networks"
    - "isolating sources of disentanglement in variational autoencoders"
    - "large scale gan training for high fidelity natural image synthesis"
    - "implicit maximum likelihood estimation"
    - "progressive growing of gans for improved quality stability and variation"
    - "learnable explicit density for continuous latent space and variational inference"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "learning controllable disentangled representations with decorrelation regularization"):
  with members:
    - "variational inference of disentangled latent concepts from unlabeled observations"
    - "unsupervised learning of disentangled representations from video"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "latent constraints learning to generate conditionally from unconditional generative models"
    - "mocogan decomposing motion and content for video generation"
    - "semantically decomposing the latent spaces of generative adversarial networks"
    - "disentangled person image generation"
    - "fader networks manipulating images by sliding attributes"

Connected through hyperedge HE-6 (induced by source paper "learning task relatedness in multi task learning for images in context"):
  with members:
    - "taskonomy disentangling task transfer learning"
    - "gnas a greedy neural architecture search method for multi attribute learning"
    - "sketchparse towards rich descriptions for poorly drawn sketches using multi task hierarchical deep networks"
    - "an overview of multi task learning in deep neural networks"
    - "omniart multi task deep learning for artistic data analysis"
    - "deep cross residual learning for multitask visual recognition"
    - "distributed multi task relationship learning"
    - "deep multi task representation learning a tensor factorisation approach"

Connected through hyperedge HE-7 (induced by source paper "uva a universal variational framework for continuous age analysis"):
  with members:
    - "global and local consistent wavelet domain age synthesis"
    - "introvae introspective variational autoencoders for photographic image synthesis"
    - "global and local consistent age generative adversarial networks"
    - "disentangled variational representation for heterogeneous face recognition"
    - "a hybrid convolutional variational autoencoder for text generation"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "quantifying facial age by posterior of age comparisons"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-8 (induced by source paper "grim repr prioritising generating important features for pseudo rehearsal"):
  with members:
    - "closed loop memory gan for continual learning"
    - "generative adversarial networks and perceptual losses for video super resolution"
    - "continual state representation learning for reinforcement learning using generative replay"
    - "pseudo rehearsal achieving deep reinforcement learning without catastrophic forgetting"
    - "deep generative dual memory network for continual learning"
    - "pseudo recursal solving the catastrophic forgetting problem in deep neural networks"
    - "continual reinforcement learning with complex synapses"
    - "progressive neural 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.CV(Computer Vision and Pattern Recognition)