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: "copy the old or paint anew an adversarial framework for non parametric image stylization"
  Abstract: Parametric generative deep models are state-of-the-art for photo and non-photo realistic image stylization. However, learning complicated image representations requires compute-intense models parametrized by a huge number of weights, which in turn requires large datasets to make learning successful. Non-parametric exemplar-based generation is a technique that works well to reproduce style from small datasets, but is also compute-intensive. These aspects are a drawback for the practice of digital AI artists: typically one wants to use a small set of stylization images, and needs a fast flexible model in order to experiment with it. With this motivation, our work has these contributions: (i) a novel stylization method called Fully Adversarial Mosaics (FAMOS) that combines the strengths of both parametric and non-parametric approaches; (ii) multiple ablations and image examples that analyze the method and show its capabilities; (iii) source code that will empower artists and machine learning researchers to use and modify FAMOS.

Connected through hyperedge HE-1 (induced by source paper "tilegan synthesis of large scale non homogeneous textures"):
  with members:
    - "multi chart generative surface modeling"
    - "carigans unpaired photo to caricature translation"
    - "non stationary texture synthesis by adversarial expansion"
    - "esrgan enhanced super resolution generative adversarial networks"
    - "multimodal unsupervised image to image translation"
    - "large scale gan training for high fidelity natural image synthesis"
    - "a style based generator architecture for generative adversarial networks"
    - "the contextual loss for image transformation with non aligned data"

Connected through hyperedge HE-2 (induced by source paper "transform the set memory attentive generation of guided and unguided image collages"):
  with members:
    - "ganosaic mosaic creation with generative texture manifolds"
    - "visual attribute transfer through deep image analogy"
    - "combining markov random fields and convolutional neural networks for image synthesis"
    - "image to image translation with conditional adversarial networks"
    - "texture synthesis with spatial generative adversarial networks"
    - "a neural algorithm of artistic style"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "spatial transformer networks"

Connected through hyperedge HE-3 (induced by source paper "dialog on a canvas with a machine"):
  with members:
    - "can creative adversarial networks generating art by learning about styles and deviating from style norms"
    - "towards the automatic anime characters creation with generative adversarial networks"
    - "artgan artwork synthesis with conditional categorical gans"
    - "a neural representation of sketch drawings"
    - "generating sequences with recurrent neural networks"

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)