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: "a content transformation block for image style transfer"
  Abstract: Style transfer has recently received a lot of attention, since it allows to study fundamental challenges in image understanding and synthesis. Recent work has significantly improved the representation of color and texture and computational speed and image resolution. The explicit transformation of image content has, however, been mostly neglected: while artistic style affects formal characteristics of an image, such as color, shape or texture, it also deforms, adds or removes content details. This paper explicitly focuses on a content-and style-aware stylization of a content image. Therefore, we introduce a content transformation module between the encoder and decoder. Moreover, we utilize similar content appearing in photographs and style samples to learn how style alters content details and we generalize this to other class details. Additionally, this work presents a novel normalization layer critical for high resolution image synthesis. The robustness and speed of our model enables a video stylization in real-time and high definition. We perform extensive qualitative and quantitative evaluations to demonstrate the validity of our approach.

Connected through hyperedge HE-1 (induced by source paper "interactive neural style transfer with artists"):
  with members:
    - "style transfer by relaxed optimal transport and self similarity"
    - "dialog on a canvas with a machine"
    - "neural painters a learned differentiable constraint for generating brushstroke paintings"
    - "wasserstein style transfer"
    - "multimodal style transfer via graph cuts"
    - "a style aware content loss for real time hd style transfer"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "synthesizing programs for images using reinforced adversarial learning"

Connected through hyperedge HE-2 (induced by source paper "style transfer for light field photography"):
  with members:
    - "style transfer by relaxed optimal transport and self similarity"
    - "artistic style transfer for videos and spherical images"
    - "learning linear transformations for fast arbitrary style transfer"
    - "stereoscopic neural style transfer"
    - "accurate light field depth estimation with superpixel regularization over partially occluded regions"
    - "characterizing and improving stability in neural style transfer"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "universal style transfer via feature transforms"

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)