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: "deep koalarization image colorization using cnns and inception resnet v2"
  Abstract: We review some of the most recent approaches to colorize gray-scale images using deep learning methods. Inspired by these, we propose a model which combines a deep Convolutional Neural Network trained from scratch with high-level features extracted from the Inception-ResNet-v2 pre-trained model. Thanks to its fully convolutional architecture, our encoder-decoder model can process images of any size and aspect ratio. Other than presenting the training results, we assess the "public acceptance" of the generated images by means of a user study. Finally, we present a carousel of applications on different types of images, such as historical photographs.

Connected through hyperedge HE-1 (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-2 (induced by source paper "nanoscale microscopy images colourisation using neural networks"):
  with members:
    - "learning representations for automatic colorization"
    - "identity mappings in deep residual networks"
    - "colorful image colorization"
    - "holistically nested edge detection"
    - "adam a method for stochastic optimization"
    - "scikit image image processing in python"
    - "going deeper with convolutions"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"

Connected through hyperedge HE-3 (induced by source paper "video colorization using cnns and keyframes extraction an application in saving bandwidth"):
  with members:
    - "learning representations for automatic colorization"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "colorful image colorization"
    - "adam a method for stochastic optimization"
    - "striving for simplicity the all convolutional net"

Connected through hyperedge HE-4 (induced by source paper "perturbation analysis of learning algorithms a unifying perspective on generation of adversarial examples"):
  with members:
    - "on the robustness of the cvpr 2018 white box adversarial example defenses"

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)