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: "contained neural style transfer for decorated logo generation"
  Abstract: Making decorated logos requires image editing skills, without sufficient skills, it could be a time-consuming task. While there are many on-line web services to make new logos, they have limited designs and duplicates can be made. We propose using neural style transfer with clip art and text for the creation of new and genuine logos. We introduce a new loss function based on distance transform of the input image, which allows the preservation of the silhouettes of text and objects. The proposed method contains style transfer to only a designated area. We demonstrate the characteristics of proposed method. Finally, we show the results of logo generation with various input images.

Connected through hyperedge HE-1 (induced by source paper "neural style difference transfer and its application to font generation"):
  with members:
    - "automatic generation of typographic font from small font subset"
    - "a closed form solution to photorealistic image stylization"
    - "the contextual loss for image transformation with non aligned data"
    - "pyramid embedded generative adversarial network for automated font generation"
    - "bringing impressionism to life with neural style transfer in come swim"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "deep photo style transfer"
    - "auto encoder guided gan for chinese calligraphy synthesis"

Connected through hyperedge HE-2 (induced by source paper "network of steel neural font style transfer from heavy metal to corporate logos"):
  with members:
    - "controllable artistic text style transfer via shape matching gan"
    - "glyphgan style consistent font generation based on generative adversarial networks"
    - "pose guided person image generation"
    - "multi style generative network for real time transfer"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "multi content gan for few shot font style transfer"
    - "image to image translation with conditional adversarial networks"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "trick or treat thematic reinforcement for artistic typography"):
  with members:
    - "logo synthesis and manipulation with clustered generative adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "multi content gan for few shot font style transfer"
    - "a neural representation of sketch drawings"
    - "fine grained analysis of sentence embeddings using auxiliary prediction tasks"
    - "image to image translation with conditional adversarial networks"
    - "character sequence models for colorfulwords"
    - "towards universal paraphrastic sentence embeddings"

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)