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: "demystifying neural style transfer"
  Abstract: Neural Style Transfer has recently demonstrated very exciting results which catches eyes in both academia and industry. Despite the amazing results, the principle of neural style transfer, especially why the Gram matrices could represent style remains unclear. In this paper, we propose a novel interpretation of neural style transfer by treating it as a domain adaptation problem. Specifically, we theoretically show that matching the Gram matrices of feature maps is equivalent to minimize the Maximum Mean Discrepancy (MMD) with the second order polynomial kernel. Thus, we argue that the essence of neural style transfer is to match the feature distributions between the style images and the generated images. To further support our standpoint, we experiment with several other distribution alignment methods, and achieve appealing results. We believe this novel interpretation connects these two important research fields, and could enlighten future researches.

Connected through hyperedge HE-1 (induced by source paper "centrality graph convolutional networks for skeleton based action recognition"):
  with members:
    - "learning class regularized features for action recognition"
    - "semantics guided neural networks for efficient skeleton based human action recognition"
    - "actional structural graph convolutional networks for skeleton based action recognition"
    - "ntu rgb d 120 a large scale benchmark for 3d human activity understanding"
    - "independently recurrent neural network indrnn building a longer and deeper rnn"
    - "real world anomaly detection in surveillance videos"
    - "sports camera calibration via synthetic data"
    - "pose flow efficient online pose tracking"

Connected through hyperedge HE-2 (induced by source paper "multi source domain adaptation for text classification via distancenet bandits"):
  with members:
    - "beyond sharing weights for deep domain adaptation"
    - "autosem automatic task selection and mixing in multi task learning"
    - "a survey of domain adaptation for neural machine translation"
    - "deep visual domain adaptation a survey"
    - "multi source domain adaptation with mixture of experts"
    - "unsupervised domain adaptation based on source guided discrepancy"
    - "deep contextualized word representations"
    - "multi source cross lingual model transfer learning what to share"

Connected through hyperedge HE-3 (induced by source paper "knowledge distillation via adaptive instance normalization"):
  with members:
    - "on the efficacy of knowledge distillation"
    - "relational knowledge distillation"
    - "adaptis adaptive instance selection network"
    - "few shot adversarial learning of realistic neural talking head models"
    - "xnor net improved binary neural networks"
    - "a comprehensive overhaul of feature distillation"
    - "contrastive representation distillation"
    - "similarity preserving knowledge distillation"

Connected through hyperedge HE-4 (induced by source paper "imaginenet restyling apps using neural style transfer"):
  with members:
    - "a closed form solution to photorealistic image stylization"
    - "learning linear transformations for fast arbitrary style transfer"
    - "arbitrary style transfer in real time with adaptive instance normalization"
    - "photorealistic style transfer with screened poisson equation"
    - "deep photo style transfer"
    - "visual attribute transfer through deep image analogy"
    - "universal style transfer via feature transforms"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-5 (induced by source paper "learning style aware symbolic music representations by adversarial autoencoders"):
  with members:
    - "midi vae modeling dynamics and instrumentation of music with applications to style transfer"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "toward controlled generation of text"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "style transfer from non parallel text by cross alignment"
    - "improved training of wasserstein gans"
    - "neural style transfer a review"
    - "deep learning techniques for music generation a survey"

Connected through hyperedge HE-6 (induced by source paper "learning a latent space of style aware symbolic music representations by adversarial autoencoders"):
  with members:
    - "midi vae modeling dynamics and instrumentation of music with applications to style transfer"
    - "a hierarchical latent vector model for learning long term structure in music"
    - "midinet a convolutional generative adversarial network for symbolic domain music generation"
    - "improved training of wasserstein gans"
    - "deep learning techniques for music generation a survey"
    - "seqgan sequence generative adversarial nets with policy gradient"
    - "c rnn gan continuous recurrent neural networks with adversarial training"
    - "deepbach a steerable model for bach chorales generation"

Connected through hyperedge HE-7 (induced by source paper "ace adapting to changing environments for semantic segmentation"):
  with members:
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "fully convolutional adaptation networks for semantic segmentation"
    - "dcan dual channel wise alignment networks for unsupervised scene adaptation"
    - "multimodal unsupervised image to image translation"
    - "reinforced continual learning"
    - "effective use of synthetic data for urban scene semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "end to end incremental learning"

Connected through hyperedge HE-8 (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"

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)