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: "diversified texture synthesis with feed forward networks"
  Abstract: Recent progresses on deep discriminative and generative modeling have shown promising results on texture synthesis. However, existing feed-forward based methods trade off generality for efficiency, which suffer from many issues, such as shortage of generality (i.e., build one network per texture), lack of diversity (i.e., always produce visually identical output) and suboptimality (i.e., generate less satisfying visual effects). In this work, we focus on solving these issues for improved texture synthesis. We propose a deep generative feed-forward network which enables efficient synthesis of multiple textures within one single network and meaningful interpolation between them. Meanwhile, a suite of important techniques are introduced to achieve better convergence and diversity. With extensive experiments, we demonstrate the effectiveness of the proposed model and techniques for synthesizing a large number of textures and show its applications with the stylization.

Connected through hyperedge HE-1 (induced by source paper "collaborative distillation for ultra resolution universal style transfer"):
  with members:
    - "relational knowledge distillation"
    - "multimodal style transfer via graph cuts"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "unpaired high resolution and scalable style transfer using generative adversarial networks"
    - "a style aware content loss for real time hd style transfer"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"

Connected through hyperedge HE-2 (induced by source paper "a content transformation block for image style transfer"):
  with members:
    - "adjustable real time style transfer"
    - "artistic style transfer for videos and spherical images"
    - "a style aware content loss for real time hd style transfer"
    - "generative adversarial networks an overview"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "bam the behance artistic media dataset for recognition beyond photography"
    - "characterizing and improving stability in neural style transfer"
    - "unpaired image to image translation using cycle consistent adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "parameter free style projection for arbitrary style transfer"):
  with members:
    - "stylenas an empirical study of neural architecture search to uncover surprisingly fast end to end universal style transfer networks"
    - "fast universal style transfer for artistic and photorealistic rendering"
    - "semantic image synthesis with spatially adaptive normalization"
    - "semantics disentangling for text to image generation"
    - "arbitrary style transfer with deep feature reshuffle"
    - "a style based generator architecture for generative adversarial networks"
    - "avatar net multi scale zero shot style transfer by feature decoration"
    - "arbitrary style transfer in real time with adaptive instance normalization"

Connected through hyperedge HE-4 (induced by source paper "ganilla generative adversarial networks for image to illustration translation"):
  with members:
    - "travelgan image to image translation by transformation vector learning"
    - "few shot unsupervised image to image translation"
    - "interactive sketch fill multiclass sketch to image translation"
    - "art2real unfolding the reality of artworks via semantically aware image to image translation"
    - "image to image translation via group wise deep whitening and coloring transformation"
    - "pros and cons of gan evaluation measures"
    - "multimodal unsupervised image to image translation"
    - "latent filter scaling for multimodal unsupervised image to image translation"

Connected through hyperedge HE-5 (induced by source paper "on demand solid texture synthesis using deep 3d networks"):
  with members:
    - "texture mixer a network for controllable synthesis and interpolation of texture"
    - "non stationary texture synthesis by adversarial expansion"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "deep image prior"
    - "two stream convolutional networks for dynamic texture synthesis"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "learning texture manifolds with the periodic spatial gan"
    - "universal style transfer via feature transforms"

Connected through hyperedge HE-6 (induced by source paper "identity preserving face recovery from stylized portraits"):
  with members:
    - "recovering faces from portraits with auxiliary facial attributes"
    - "identity preserving face recovery from portraits"
    - "a deeper look at power normalizations"
    - "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "multi style generative network for real time transfer"
    - "high quality facial photo sketch synthesis using multi adversarial networks"
    - "arbitrary style transfer in real time with adaptive instance normalization"

Connected through hyperedge HE-7 (induced by source paper "recovering faces from portraits with auxiliary facial attributes"):
  with members:
    - "gp gan gender preserving gan for synthesizing faces from landmarks"
    - "identity preserving face recovery from portraits"
    - "a deeper look at power normalizations"
    - "stable and controllable neural texture synthesis and style transfer using histogram losses"
    - "high quality facial photo sketch synthesis using multi adversarial networks"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"
    - "image de raining using a conditional generative adversarial network"

Connected through hyperedge HE-8 (induced by source paper "gated gan adversarial gated networks for multi collection style transfer"):
  with members:
    - "activation maximization generative adversarial nets"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "deep photo style transfer"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "style transfer via texture synthesis"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"

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)