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: "unsupervised diverse colorization via generative adversarial networks"
  Abstract: Colorization of grayscale images has been a hot topic in computer vision. Previous research mainly focuses on producing a colored image to match the original one. However, since many colors share the same gray value, an input grayscale image could be diversely colored while maintaining its reality. In this paper, we design a novel solution for unsupervised diverse colorization. Specifically, we leverage conditional generative adversarial networks to model the distribution of real-world item colors, in which we develop a fully convolutional generator with multi-layer noise to enhance diversity, with multi-layer condition concatenation to maintain reality, and with stride 1 to keep spatial information. With such a novel network architecture, the model yields highly competitive performance on the open LSUN bedroom dataset. The Turing test of 80 humans further indicates our generated color schemes are highly convincible.

Connected through hyperedge HE-1 (induced by source paper "chromagan adversarial picture colorization with semantic class distribution"):
  with members:
    - "semantic image inpainting through improved wasserstein generative adversarial networks"
    - "a style based generator architecture for generative adversarial networks"
    - "ganimation anatomically aware facial animation from a single image"
    - "deep exemplar based colorization"
    - "image colorization using generative adversarial networks"
    - "everybody dance now"
    - "real time user guided image colorization with learned deep priors"
    - "adversarial ranking for language generation"

Connected through hyperedge HE-2 (induced by source paper "robust perceptual night vision in thermal colorization"):
  with members:
    - "multimodal sensor fusion in single thermal image super resolution"
    - "pixcolor pixel recursive colorization"
    - "tv gan generative adversarial network based thermal to visible face recognition"
    - "learning representations for automatic colorization"
    - "infrared colorization using deep convolutional neural networks"
    - "image to image translation with conditional adversarial networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-3 (induced by source paper "attribute guided sketch generation"):
  with members:
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "gesturegan for hand gesture to gesture translation in the wild"
    - "instagan instance aware image to image translation"
    - "r btn cross domain face composite and synthesis from limited facial patches"
    - "deformable gans for pose based human image generation"
    - "image generation from sketch constraint using contextual gan"
    - "unsupervised creation of parameterized avatars"
    - "semi supervised learning with context conditional generative adversarial networks"

Connected through hyperedge HE-4 (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-5 (induced by source paper "adversarial colorization of icons based on structure and color conditions"):
  with members:
    - "user guided deep anime line art colorization with conditional adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "conditional image to image translation"
    - "semi supervised fusedgan for conditional image generation"
    - "began boundary equilibrium generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved training of wasserstein gans"
    - "mocogan decomposing motion and content for video generation"

Connected through hyperedge HE-6 (induced by source paper "metalgan a cluster based adaptive training for few shot adversarial colorization"):
  with members:
    - "semantic image synthesis with spatially adaptive normalization"
    - "on first order meta learning algorithms"
    - "learning to optimize neural nets"
    - "a zero shot framework for sketch based image retrieval"
    - "texturegan controlling deep image synthesis with texture patches"
    - "rl 2 fast reinforcement learning via slow reinforcement learning"
    - "learned optimizers that scale and generalize"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-7 (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"
    - "film visual reasoning with a general conditioning layer"

Connected through hyperedge HE-8 (induced by source paper "chromagan an adversarial approach for picture colorization"):
  with members:
    - "semantic image inpainting through improved wasserstein generative adversarial networks"
    - "a style based generator architecture for generative adversarial networks"
    - "ganimation anatomically aware facial animation from a single image"
    - "deep exemplar based colorization"
    - "image colorization using generative adversarial networks"
    - "everybody dance now"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "real time user guided image colorization with learned deep priors"

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)