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: "synthesising dynamic textures using convolutional neural networks"
  Abstract: Here we present a parametric model for dynamic textures. The model is based on spatiotemporal summary statistics computed from the feature representations of a Convolutional Neural Network (CNN) trained on object recognition. We demonstrate how the model can be used to synthesise new samples of dynamic textures and to predict motion in simple movies.

Connected through hyperedge HE-1 (induced by source paper "learning energy based spatial temporal generative convnets for dynamic patterns"):
  with members:
    - "videoflow a flow based generative model for video"
    - "learning descriptor networks for 3d shape synthesis and analysis"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "wasserstein introspective neural networks"
    - "two stream convolutional networks for dynamic texture synthesis"
    - "introspective classification with convolutional nets"
    - "the kinetics human action video dataset"
    - "mocogan decomposing motion and content for video generation"

Connected through hyperedge HE-2 (induced by source paper "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"):
  with members:
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "benchmarking neural network robustness to common corruptions and perturbations"
    - "the unreasonable effectiveness of texture transfer for single image super resolution"
    - "assessing shape bias property of convolutional neural networks"
    - "improving shape deformation in unsupervised image to image translation"
    - "one shot texture segmentation"
    - "generalisation in humans and deep neural networks"
    - "arbitrary style transfer in real time with adaptive instance normalization"

Connected through hyperedge HE-3 (induced by source paper "learning to generate time lapse videos using multi stage dynamic generative adversarial networks"):
  with members:
    - "tagging like humans diverse and distinct image annotation"
    - "decomposing motion and content for natural video sequence prediction"
    - "learning to generate long term future via hierarchical prediction"
    - "two stream convolutional networks for dynamic texture synthesis"
    - "perceptual generative adversarial networks for small object detection"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "mocogan decomposing motion and content for video generation"
    - "wasserstein learning of deep generative point process models"

Connected through hyperedge HE-4 (induced by source paper "two stream convolutional networks for dynamic texture synthesis"):
  with members:
    - "photographic image synthesis with cascaded refinement networks"
    - "perceptual losses for real time style transfer and super resolution"
    - "optical flow estimation using a spatial pyramid network"
    - "texture networks feed forward synthesis of textures and stylized images"
    - "semantic style transfer and turning two bit doodles into fine artworks"
    - "back to basics unsupervised learning of optical flow via brightness constancy and motion smoothness"
    - "artistic style transfer for videos"
    - "controlling perceptual factors in neural style transfer"

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)