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: "multimodal transfer a hierarchical deep convolutional neural network for fast artistic style transfer"
  Abstract: Transferring artistic styles onto everyday photographs has become an extremely popular task in both academia and industry. Recently, offline training has replaced on-line iterative optimization, enabling nearly real-time stylization. When those stylization networks are applied directly to high-resolution images, however, the style of localized regions often appears less similar to the desired artistic style. This is because the transfer process fails to capture small, intricate textures and maintain correct texture scales of the artworks. Here we propose a multimodal convolutional neural network that takes into consideration faithful representations of both color and luminance channels, and performs stylization hierarchically with multiple losses of increasing scales. Compared to state-of-the-art networks, our network can also perform style transfer in nearly real-time by conducting much more sophisticated training offline. By properly handling style and texture cues at multiple scales using several modalities, we can transfer not just large-scale, obvious style cues but also subtle, exquisite ones. That is, our scheme can generate results that are visually pleasing and more similar to multiple desired artistic styles with color and texture cues at multiple scales.

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"
    - "diversified texture synthesis with feed forward networks"
    - "bam the behance artistic media dataset for recognition beyond photography"
    - "characterizing and improving stability in neural style transfer"

Connected through hyperedge HE-3 (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-4 (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"
    - "diversified texture synthesis with feed forward 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"

Connected through hyperedge HE-5 (induced by source paper "fast universal style transfer for artistic and photorealistic rendering"):
  with members:
    - "a closed form solution to photorealistic image stylization"
    - "multimodal unsupervised image to image translation"
    - "arbitrary style transfer with deep feature reshuffle"
    - "avatar net multi scale zero shot style transfer by feature decoration"
    - "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"
    - "deep layer aggregation"
    - "arbitrary style transfer in real time with adaptive instance normalization"

Connected through hyperedge HE-6 (induced by source paper "style mixer semantic aware multi style transfer network"):
  with members:
    - "attention aware multi stroke style transfer"
    - "arbitrary style transfer with style attentional networks"
    - "learning linear transformations for fast arbitrary style transfer"
    - "metastyle three way trade off among speed flexibility and quality in neural style transfer"
    - "arbitrary style transfer with deep feature reshuffle"
    - "the contextual loss for image transformation with non aligned data"
    - "avatar net multi scale zero shot style transfer by feature decoration"
    - "non local neural networks"

Connected through hyperedge HE-7 (induced by source paper "fingerprint spoof generalization"):
  with members:
    - "biometric presentation attack detection beyond the visible spectrum"
    - "universal material translator towards spoof fingerprint generalization"
    - "generalizing fingerprint spoof detector learning a one class classifier"
    - "oct fingerprints resilience to presentation attacks"
    - "livdet in action fingerprint liveness detection competition 2019"
    - "fingerprint presentation attack detection based on local features encoding for unknown attacks"
    - "livdet 2017 fingerprint liveness detection competition 2017"
    - "fingerprint presentation attack detection generalization and efficiency"

Connected through hyperedge HE-8 (induced by source paper "styleremix an interpretable representation for neural image style transfer"):
  with members:
    - "a style aware content loss for real time hd style transfer"
    - "learning linear transformations for fast arbitrary style transfer"
    - "arbitrary style transfer with deep feature reshuffle"
    - "multi style generative network for real time transfer"
    - "diversified texture synthesis with feed forward networks"
    - "learning to discover cross domain relations with generative adversarial networks"
    - "exploring the structure of a real time arbitrary neural artistic stylization network"
    - "unpaired image to image translation using cycle consistent adversarial 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)