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: "meta networks for neural style transfer"
  Abstract: In this paper we propose a new method to get the specified network parameters through one time feed-forward propagation of the meta networks and explore the application to neural style transfer. Recent works on style transfer typically need to train image transformation networks for every new style, and the style is encoded in the network parameters by enormous iterations of stochastic gradient descent. To tackle these issues, we build a meta network which takes in the style image and produces a corresponding image transformations network directly. Compared with optimization-based methods for every style, our meta networks can handle an arbitrary new style within $19ms$ seconds on one modern GPU card. The fast image transformation network generated by our meta network is only 449KB, which is capable of real-time executing on a mobile device. We also investigate the manifold of the style transfer networks by operating the hidden features from meta networks. Experiments have well validated the effectiveness of our method. Code and trained models has been released this https URL

Connected through hyperedge HE-1 (induced by source paper "etnet error transition network for arbitrary style transfer"):
  with members:
    - "attention aware multi stroke style transfer"
    - "a style aware content loss for real time hd style transfer"
    - "learning linear transformations for fast arbitrary style transfer"
    - "difnet semantic segmentation by diffusion networks"
    - "style separation and synthesis via generative adversarial networks"
    - "deep back projection networks for super resolution"
    - "avatar net multi scale zero shot style transfer by feature decoration"
    - "nonlocal neural networks nonlocal diffusion and nonlocal modeling"

Connected through hyperedge HE-2 (induced by source paper "a style aware content loss for real time hd style transfer"):
  with members:
    - "stroke controllable fast style transfer with adaptive receptive fields"
    - "a variational u net for conditional appearance and shape generation"
    - "generative adversarial networks an overview"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "diversified texture synthesis with feed forward networks"

Connected through hyperedge HE-3 (induced by source paper "pair wise exchangeable feature extraction for arbitrary style transfer"):
  with members:
    - "multi marginal wasserstein gan"
    - "attention aware multi stroke style transfer"
    - "etnet error transition network for arbitrary style transfer"
    - "adversarial learning with local coordinate coding"
    - "arbitrary style transfer with style attentional networks"
    - "a style aware content loss for real time hd style transfer"
    - "multimodal unsupervised image to image translation"
    - "exemplar guided unsupervised image to image translation with semantic consistency"

Connected through hyperedge HE-4 (induced by source paper "avatar net multi scale zero shot style transfer by feature decoration"):
  with members:
    - "stereoscopic neural style transfer"
    - "demystifying neural style transfer"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "multi style generative network for real time transfer"
    - "diversified texture synthesis with feed forward networks"
    - "deep photo style transfer"
    - "style transfer via texture synthesis"
    - "characterizing and improving stability in neural style transfer"

Connected through hyperedge HE-5 (induced by source paper "arbitrary style transfer with style attentional networks"):
  with members:
    - "non local neural networks"
    - "demystifying neural style transfer"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "attention is all you need"
    - "multi style generative network for real time transfer"
    - "diversified texture synthesis with feed forward networks"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "zm net real time zero shot image manipulation network"

Connected through hyperedge HE-6 (induced by source paper "efanet exchangeable feature alignment network for arbitrary style transfer"):
  with members:
    - "multi marginal wasserstein gan"
    - "attention aware multi stroke style transfer"
    - "etnet error transition network for arbitrary style transfer"
    - "arbitrary style transfer with style attentional networks"
    - "a style aware content loss for real time hd style transfer"
    - "multimodal unsupervised image to image translation"
    - "style separation and synthesis via generative adversarial networks"
    - "dida disentangled synthesis for domain adaptation"

Connected through hyperedge HE-7 (induced by source paper "neural comic style transfer case study"):
  with members:
    - "improved style transfer by respecting inter layer correlations"
    - "demystifying neural style transfer"
    - "neural style transfer a review"
    - "fast patch based style transfer of arbitrary style"

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)