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: "tag2pix line art colorization using text tag with secat and changing loss"
  Abstract: Line art colorization is expensive and challenging to automate. A GAN approach is proposed, called Tag2Pix, of line art colorization which takes as input a grayscale line art and color tag information and produces a quality colored image. First, we present the Tag2Pix line art colorization dataset. A generator network is proposed which consists of convolutional layers to transform the input line art, a pre-trained semantic extraction network, and an encoder for input color information. The discriminator is based on an auxiliary classifier GAN to classify the tag information as well as genuineness. In addition, we propose a novel network structure called SECat, which makes the generator properly colorize even small features such as eyes, and also suggest a novel two-step training method where the generator and discriminator first learn the notion of object and shape and then, based on the learned notion, learn colorization, such as where and how to place which color. We present both quantitative and qualitative evaluations which prove the effectiveness of the proposed method.

Connected through hyperedge HE-1 (induced by source paper "learning to shade hand drawn sketches"):
  with members:
    - "im2pencil controllable pencil illustration from photographs"
    - "single image portrait relighting"
    - "an intriguing failing of convolutional neural networks and the coordconv solution"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "comicolorization semi automatic manga colorization"
    - "real time user guided image colorization with learned deep priors"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "mastering sketching adversarial augmentation for structured prediction"

Connected through hyperedge HE-2 (induced by source paper "stargan v2 diverse image synthesis for multiple domains"):
  with members:
    - "mode seeking generative adversarial networks for diverse image synthesis"
    - "coloring with limited data few shot colorization via memory augmented networks"
    - "multi mapping image to image translation via learning disentanglement"
    - "large scale adversarial representation learning"
    - "few shot unsupervised image to image translation"
    - "sdit scalable and diverse cross domain image translation"
    - "semantic image synthesis with spatially adaptive normalization"
    - "high fidelity image generation with fewer labels"

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)