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: "coloring with words guiding image colorization through text based palette generation"
  Abstract: This paper proposes a novel approach to generate multiple color palettes that reflect the semantics of input text and then colorize a given grayscale image according to the generated color palette. In contrast to existing approaches, our model can understand rich text, whether it is a single word, a phrase, or a sentence, and generate multiple possible palettes from it. For this task, we introduce our manually curated dataset called Palette-and-Text (PAT). Our proposed model called Text2Colors consists of two conditional generative adversarial networks: the text-to-palette generation networks and the palette-based colorization networks. The former captures the semantics of the text input and produce relevant color palettes. The latter colorizes a grayscale image using the generated color palette. Our evaluation results show that people preferred our generated palettes over ground truth palettes and that our model can effectively reflect the given palette when colorizing an image.

Connected through hyperedge HE-1 (induced by source paper "what and where to translate local mask based image to image translation"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "elegant exchanging latent encodings with gan for transferring multiple face attributes"
    - "conditional image to image translation"
    - "batch instance normalization for adaptively style invariant neural networks"
    - "unsupervised attention guided image to image translation"
    - "ganimation anatomically aware facial animation from a single image"

Connected through hyperedge HE-2 (induced by source paper "coloring with limited data few shot colorization via memory augmented networks"):
  with members:
    - "a memory network approach for story based temporal summarization of 360 deg videos"
    - "memorization precedes generation learning unsupervised gans with memory networks"
    - "image to image translation via group wise deep whitening and coloring transformation"
    - "abstractive summarization of reddit posts with multi level memory networks"
    - "multimodal unsupervised image to image translation"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "ganimation anatomically aware facial animation from a single image"
    - "arbitrary style transfer in real time with adaptive instance normalization"

Connected through hyperedge HE-3 (induced by source paper "miso mutual information loss with stochastic style representations for multimodal image to image translation"):
  with members:
    - "augmented cyclegan learning many to many mappings from unpaired data"
    - "multimodal unsupervised image to image translation"
    - "spectral normalization for generative adversarial networks"
    - "large scale gan training for high fidelity natural image synthesis"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "dist gan an improved gan using distance constraints"
    - "a style based generator architecture for generative adversarial networks"
    - "mine mutual information neural estimation"

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)