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: "probabilistic semantic inpainting with pixel constrained cnns"
  Abstract: Semantic inpainting is the task of inferring missing pixels in an image given surrounding pixels and high level image semantics. Most semantic inpainting algorithms are deterministic: given an image with missing regions, a single inpainted image is generated. However, there are often several plausible inpaintings for a given missing region. In this paper, we propose a method to perform probabilistic semantic inpainting by building a model, based on PixelCNNs, that learns a distribution of images conditioned on a subset of visible pixels. Experiments on the MNIST and CelebA datasets show that our method produces diverse and realistic inpaintings.

Connected through hyperedge HE-1 (induced by source paper "pi rec progressive image reconstruction network with edge and color domain"):
  with members:
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "sc fegan face editing generative adversarial network with user s sketch and color"
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "spg net segmentation prediction and guidance network for image inpainting"
    - "smit stochastic multi label image to image translation"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "deepflow history matching in the space of deep generative models"):
  with members:
    - "cyclical stochastic gradient mcmc for bayesian deep learning"
    - "cgans with projection discriminator"
    - "analyzing inverse problems with invertible neural networks"
    - "adversarial regularizers in inverse problems"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "sampling generative networks"
    - "improved training of wasserstein gans"
    - "reconstruction of three dimensional porous media using generative adversarial 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)