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: "image colorization using generative adversarial networks"
  Abstract: Over the last decade, the process of automatic image colorization has been of significant interest for several application areas including restoration of aged or degraded images. This problem is highly ill-posed due to the large degrees of freedom during the assignment of color information. Many of the recent developments in automatic colorization involve images that contain a common theme or require highly processed data such as semantic maps as input. In our approach, we attempt to fully generalize the colorization procedure using a conditional Deep Convolutional Generative Adversarial Network (DCGAN). The network is trained over datasets that are publicly available such as CIFAR-10 and Places365. The results between the generative model and traditional deep neural networks are compared.

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"
    - "everybody dance now"
    - "real time user guided image colorization with learned deep priors"
    - "adversarial ranking for language generation"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-2 (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"
    - "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"
    - "adversarial ranking for language generation"

Connected through hyperedge HE-3 (induced by source paper "automatic temporally coherent video colorization"):
  with members:
    - "edgeconnect generative image inpainting with adversarial edge learning"
    - "the unreasonable effectiveness of texture transfer for single image super resolution"
    - "user guided deep anime line art colorization with conditional adversarial networks"
    - "spectral normalization for generative adversarial networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "everybody dance now"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-4 (induced by source paper "deep model transferability from attribution maps"):
  with members:
    - "taskonomy disentangling task transfer learning"
    - "joint 3d face reconstruction and dense alignment with position map regression network"
    - "generative image inpainting with contextual attention"
    - "progressive growing of gans for improved quality stability and variation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "deeper depth prediction with fully convolutional residual networks"
    - "finding tiny faces"

Connected through hyperedge HE-5 (induced by source paper "perceptual conditional generative adversarial networks for end to end image colourization"):
  with members:
    - "image de raining using a conditional generative adversarial network"
    - "perceptual losses for real time style transfer and super resolution"
    - "identity mappings in deep residual networks"
    - "image to image translation with conditional adversarial networks"
    - "colorful image colorization"
    - "u net convolutional networks for biomedical image segmentation"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "very deep convolutional networks for large scale image recognition"

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)