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 image colorization"
  Abstract: We develop a probabilistic technique for colorizing grayscale natural images. In light of the intrinsic uncertainty of this task, the proposed probabilistic framework has numerous desirable properties. In particular, our model is able to produce multiple plausible and vivid colorizations for a given grayscale image and is one of the first colorization models to provide a proper stochastic sampling scheme. Moreover, our training procedure is supported by a rigorous theoretical framework that does not require any ad hoc heuristics and allows for efficient modeling and learning of the joint pixel color distribution. We demonstrate strong quantitative and qualitative experimental results on the CIFAR-10 dataset and the challenging ILSVRC 2012 dataset.

Connected through hyperedge HE-1 (induced by source paper "guided image generation with conditional invertible neural networks"):
  with members:
    - "videoflow a flow based generative model for video"
    - "semantic image synthesis with spatially adaptive normalization"
    - "cgans with projection discriminator"
    - "analyzing inverse problems with invertible neural networks"
    - "training generative reversible networks"
    - "invertible residual networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "excessive invariance causes adversarial vulnerability"

Connected through hyperedge HE-2 (induced by source paper "deep exemplar based video colorization"):
  with members:
    - "the relativistic discriminator a key element missing from standard gan"
    - "deep video color propagation"
    - "learning blind video temporal consistency"
    - "spectral normalization for generative adversarial networks"
    - "switchable temporal propagation network"
    - "deep exemplar based colorization"
    - "the contextual loss for image transformation with non aligned data"
    - "pixel level semantics guided image colorization"

Connected through hyperedge HE-3 (induced by source paper "pixelated semantic colorization"):
  with members:
    - "recovering realistic texture in image super resolution by deep spatial feature transform"
    - "deep exemplar based colorization"
    - "pixel level semantics guided image colorization"
    - "tracking emerges by colorizing videos"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "pixcolor pixel recursive colorization"
    - "real time user guided image colorization with learned deep priors"
    - "unsupervised diverse colorization via generative adversarial networks"

Connected through hyperedge HE-4 (induced by source paper "structural consistency and controllability for diverse colorization"):
  with members:
    - "pixcolor pixel recursive colorization"
    - "real time user guided image colorization with learned deep priors"
    - "diverse and accurate image description using a variational auto encoder with an additive gaussian encoding space"
    - "creativity generating diverse questions using variational autoencoders"
    - "unsupervised diverse colorization via generative adversarial networks"
    - "toward multimodal image to image translation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "learning representations for automatic colorization"

Connected through hyperedge HE-5 (induced by source paper "thermal infrared colorization via conditional generative adversarial network"):
  with members:
    - "dslr quality photos on mobile devices with deep convolutional networks"
    - "semantic image synthesis via adversarial learning"
    - "wespe weakly supervised photo enhancer for digital cameras"
    - "pixcolor pixel recursive colorization"
    - "unsupervised diverse colorization via generative adversarial networks"
    - "stylebank an explicit representation for neural image style transfer"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "unsupervised cross domain image generation"

Connected through hyperedge HE-6 (induced by source paper "pixel level semantics guided image colorization"):
  with members:
    - "pixcolor pixel recursive colorization"
    - "real time user guided image colorization with learned deep priors"
    - "unsupervised diverse colorization via generative adversarial networks"
    - "outline colorization through tandem adversarial networks"
    - "learning representations for automatic colorization"
    - "infrared colorization using deep convolutional neural networks"
    - "image to image translation with conditional adversarial networks"
    - "colorful image colorization"

Connected through hyperedge HE-7 (induced by source paper "pixcolor pixel recursive colorization"):
  with members:
    - "fast generation for convolutional autoregressive models"
    - "pixelcnn improving the pixelcnn with discretized logistic mixture likelihood and other modifications"
    - "real time user guided image colorization with learned deep priors"
    - "unsupervised diverse colorization via generative adversarial networks"
    - "pixel recursive super resolution"
    - "outline colorization through tandem adversarial networks"
    - "learning representations for automatic colorization"
    - "conditional image generation with pixelcnn decoders"

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)