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: "to create what you tell generating videos from captions"
  Abstract: We are creating multimedia contents everyday and everywhere. While automatic content generation has played a fundamental challenge to multimedia community for decades, recent advances of deep learning have made this problem feasible. For example, the Generative Adversarial Networks (GANs) is a rewarding approach to synthesize images. Nevertheless, it is not trivial when capitalizing on GANs to generate videos. The difficulty originates from the intrinsic structure where a video is a sequence of visually coherent and semantically dependent frames. This motivates us to explore semantic and temporal coherence in designing GANs to generate videos. In this paper, we present a novel Temporal GANs conditioning on Captions, namely TGANs-C, in which the input to the generator network is a concatenation of a latent noise vector and caption embedding, and then is transformed into a frame sequence with 3D spatio-temporal convolutions. Unlike the naive discriminator which only judges pairs as fake or real, our discriminator additionally notes whether the video matches the correct caption. In particular, the discriminator network consists of three discriminators: video discriminator classifying realistic videos from generated ones and optimizes video-caption matching, frame discriminator discriminating between real and fake frames and aligning frames with the conditioning caption, and motion discriminator emphasizing the philosophy that the adjacent frames in the generated videos should be smoothly connected as in real ones. We qualitatively demonstrate the capability of our TGANs-C to generate plausible videos conditioning on the given captions on two synthetic datasets (SBMG and TBMG) and one real-world dataset (MSVD). Moreover, quantitative experiments on MSVD are performed to validate our proposal via Generative Adversarial Metric and human 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"
    - "image generation from scene graphs"

Connected through hyperedge HE-2 (induced by source paper "vision and language from visual perception to content creation"):
  with members:
    - "spatio temporal dynamics and semantic attribute enriched visual encoding for video captioning"
    - "hierarchy parsing for image captioning"
    - "unsupervised person image generation with semantic parsing transformation"
    - "temporal deformable convolutional encoder decoder networks for video captioning"
    - "generating multiple objects at spatially distinct locations"
    - "joint discriminative and generative learning for person re identification"
    - "relationship aware spatial perception fusion for realistic scene layout generation"
    - "photo realistic facial details synthesis from single image"

Connected through hyperedge HE-3 (induced by source paper "video to video translation for visual speech synthesis"):
  with members:
    - "video to video synthesis"
    - "artistic style transfer for videos and spherical images"
    - "talking face generation by adversarially disentangled audio visual representation"
    - "multimodal unsupervised image to image translation"
    - "ganimation anatomically aware facial animation from a single image"
    - "began boundary equilibrium generative adversarial networks"
    - "gans trained by a two time scale update rule converge to a nash equilibrium"
    - "progressive growing of gans for improved quality stability and variation"

Connected through hyperedge HE-4 (induced by source paper "mocycle gan unpaired video to video translation"):
  with members:
    - "video to video synthesis"
    - "reconet real time coherent video style transfer network"
    - "deep semantic hashing with generative adversarial networks"
    - "recycle gan unsupervised video retargeting"
    - "jointly localizing and describing events for dense video captioning"
    - "unsupervised attention guided image to image translation"
    - "multi style generative network for real time transfer"
    - "learning to discover cross domain relations with generative adversarial networks"

Connected through hyperedge HE-5 (induced by source paper "end to end video captioning"):
  with members:
    - "less is more picking informative frames for video captioning"
    - "reconstruction network for video captioning"
    - "show and tell lessons learned from the 2015 mscoco image captioning challenge"
    - "deep learning for video classification and captioning"
    - "video captioning with transferred semantic attributes"
    - "semantic compositional networks for visual captioning"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "knowing when to look adaptive attention via a visual sentinel for image captioning"

Connected through hyperedge HE-6 (induced by source paper "neural storyboard artist visualizing stories with coherent image sequences"):
  with members:
    - "videobert a joint model for video and language representation learning"
    - "retrieve and refine improved sequence generation models for dialogue"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "stacked cross attention for image text matching"
    - "learning word vectors for 157 languages"
    - "beyond narrative description generating poetry from images by multi adversarial training"
    - "storygan a sequential conditional gan for story visualization"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "trimmed action recognition dense captioning events in videos and spatio temporal action localization with focus on activitynet challenge 2019"):
  with members:
    - "temporal deformable convolutional encoder decoder networks for video captioning"
    - "learning spatio temporal representation with local and global diffusion"
    - "pointing novel objects in image captioning"
    - "jointly localizing and describing events for dense video captioning"
    - "exploring visual relationship for image captioning"
    - "attention is all you need"
    - "incorporating copying mechanism in image captioning for learning novel objects"
    - "deformable convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "actions generation from captions"):
  with members:
    - "deep communicating agents for abstractive summarization"
    - "qanet combining local convolution with global self attention for reading comprehension"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "bridging nonlinearities and stochastic regularizers with gaussian error linear units"
    - "adversarial neural machine translation"
    - "attention is all you need"
    - "get to the point summarization with pointer generator networks"
    - "improving neural machine translation with conditional sequence generative adversarial nets"

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)