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: "two stream convolutional networks for dynamic texture synthesis"
  Abstract: We introduce a two-stream model for dynamic texture synthesis. Our model is based on pre-trained convolutional networks (ConvNets) that target two independent tasks: (i) object recognition, and (ii) optical flow prediction. Given an input dynamic texture, statistics of filter responses from the object recognition ConvNet encapsulate the per-frame appearance of the input texture, while statistics of filter responses from the optical flow ConvNet model its dynamics. To generate a novel texture, a randomly initialized input sequence is optimized to match the feature statistics from each stream of an example texture. Inspired by recent work on image style transfer and enabled by the two-stream model, we also apply the synthesis approach to combine the texture appearance from one texture with the dynamics of another to generate entirely novel dynamic textures. We show that our approach generates novel, high quality samples that match both the framewise appearance and temporal evolution of input texture. Finally, we quantitatively evaluate our texture synthesis approach with a thorough user study.

Connected through hyperedge HE-1 (induced by source paper "intelligent home 3d automatic 3d house design from linguistic descriptions only"):
  with members:
    - "multi marginal wasserstein gan"
    - "object driven text to image synthesis via adversarial training"
    - "collaborative unsupervised domain adaptation for medical image diagnosis"
    - "auto embedding generative adversarial networks for high resolution image synthesis"
    - "semantics disentangling for text to image generation"
    - "nat neural architecture transformer for accurate and compact architectures"
    - "text2scene generating compositional scenes from textual descriptions"
    - "to create what you tell generating videos from captions"

Connected through hyperedge HE-2 (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"
    - "diversified texture synthesis with feed forward networks"
    - "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-3 (induced by source paper "dynamic variational autoencoders for visual process modeling"):
  with members:
    - "alignment distances on systems of bags"
    - "dyan a dynamical atoms based network for video prediction"
    - "visual dynamics probabilistic future frame synthesis via cross convolutional networks"
    - "generating videos with scene dynamics"
    - "embed to control a locally linear latent dynamics model for control from raw images"
    - "deep predictive coding networks"

Connected through hyperedge HE-4 (induced by source paper "inserting videos into videos"):
  with members:
    - "video to video synthesis"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "recycle gan unsupervised video retargeting"
    - "artistic style transfer for videos and spherical images"
    - "yolov3 an incremental improvement"
    - "pedestrian synthesis gan generating pedestrian data in real scene and beyond"
    - "multimodal unsupervised image to image translation"
    - "context aware synthesis and placement of object instances"

Connected through hyperedge HE-5 (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"
    - "introspective classification with convolutional nets"
    - "the kinetics human action video dataset"
    - "mocogan decomposing motion and content for video generation"
    - "synthesising dynamic textures using convolutional neural networks"

Connected through hyperedge HE-6 (induced by source paper "conditional generative convnets for exemplar based texture synthesis"):
  with members:
    - "learning the synthesizability of dynamic texture samples"
    - "learning descriptor networks for 3d shape synthesis and analysis"
    - "spectral normalization for generative adversarial networks"
    - "generative image inpainting with contextual attention"
    - "a survey of exemplar based texture synthesis"
    - "texture characterization using shape co occurrence patterns"
    - "improved training of wasserstein gans"
    - "improved texture networks maximizing quality and diversity in feed forward stylization and texture synthesis"

Connected through hyperedge HE-7 (induced by source paper "motion based generator model unsupervised disentanglement of appearance trackable and intrackable motions in dynamic patterns"):
  with members:
    - "mocogan decomposing motion and content for video generation"
    - "high resolution image synthesis and semantic manipulation with conditional gans"
    - "disentangling factors of variation in deep representations using adversarial training"
    - "temporal generative adversarial nets with singular value clipping"
    - "generating videos with scene dynamics"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "video primal sketch a unified middle level representation for video"

Connected through hyperedge HE-8 (induced by source paper "similarity dt kernel similarity embedding for dynamic texture synthesis"):
  with members:
    - "joint discriminative and generative learning for person re identification"
    - "towards optimal discrete online hashing with balanced similarity"
    - "semi supervised feature learning for improving writer identification"
    - "learning energy based spatial temporal generative convnets for dynamic patterns"
    - "in defense of the triplet loss for person re identification"
    - "photographic image synthesis with cascaded refinement networks"
    - "temporal generative adversarial nets with singular value clipping"
    - "learning temporal transformations from time lapse videos"

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)