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: "adversarial training of variational auto encoders for high fidelity image generation"
  Abstract: Variational auto-encoders (VAEs) provide an attractive solution to image generation problem. However, they tend to produce blurred and over-smoothed images due to their dependence on pixel-wise reconstruction loss. This paper introduces a new approach to alleviate this problem in the VAE based generative models. Our model simultaneously learns to match the data, reconstruction loss and the latent distributions of real and fake images to improve the quality of generated samples. To compute the loss distributions, we introduce an auto-encoder based discriminator model which allows an adversarial learning procedure. The discriminator in our model also provides perceptual guidance to the VAE by matching the learned similarity metric of the real and fake samples in the latent space. To stabilize the overall training process, our model uses an error feedback approach to maintain the equilibrium between competing networks in the model. Our experiments show that the generated samples from our proposed model exhibit a diverse set of attributes and facial expressions and scale up to high-resolution images very well.

Connected through hyperedge HE-1 (induced by source paper "representation learning on unit ball with 3d roto translational equivariance"):
  with members:
    - "blended convolution and synthesis for efficient discrimination of 3d shapes"
    - "n body networks a covariant hierarchical neural network architecture for learning atomic potentials"
    - "attention based deep multiple instance learning"
    - "splatnet sparse lattice networks for point cloud processing"
    - "so net self organizing network for point cloud analysis"
    - "cubenet equivariance to 3d rotation and translation"
    - "dynamic graph cnn for learning on point clouds"
    - "a general theory of equivariant cnns on homogeneous spaces"

Connected through hyperedge HE-2 (induced by source paper "blended convolution and synthesis for efficient discrimination of 3d shapes"):
  with members:
    - "group normalization"
    - "point convolutional neural networks by extension operators"
    - "local spectral graph convolution for point set feature learning"
    - "so net self organizing network for point cloud analysis"
    - "dynamic graph cnn for learning on point clouds"
    - "3d steerable cnns learning rotationally equivariant features in volumetric data"
    - "volumetric convolution automatic representation learning in unit ball"
    - "escape from cells deep kd networks for the recognition of 3d point cloud models"

Connected through hyperedge HE-3 (induced by source paper "escaping from collapsing modes in a constrained space"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"
    - "generative face completion"
    - "adversarial discriminative domain adaptation"
    - "towards diverse and natural image descriptions via a conditional gan"
    - "unsupervised pixel level domain adaptation with generative adversarial 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)