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: "imagine this scripts to compositions to videos"
  Abstract: Imagining a scene described in natural language with realistic layout and appearance of entities is the ultimate test of spatial, visual, and semantic world knowledge. Towards this goal, we present the Composition, Retrieval, and Fusion Network (CRAFT), a model capable of learning this knowledge from video-caption data and applying it while generating videos from novel captions. CRAFT explicitly predicts a temporal-layout of mentioned entities (characters and objects), retrieves spatio-temporal entity segments from a video database and fuses them to generate scene videos. Our contributions include sequential training of components of CRAFT while jointly modeling layout and appearances, and losses that encourage learning compositional representations for retrieval. We evaluate CRAFT on semantic fidelity to caption, composition consistency, and visual quality. CRAFT outperforms direct pixel generation approaches and generalizes well to unseen captions and to unseen video databases with no text annotations. We demonstrate CRAFT on FLINTSTONES, a new richly annotated video-caption dataset with over 25000 videos. For a glimpse of videos generated by CRAFT, see this https URL

Connected through hyperedge HE-1 (induced by source paper "layoutvae stochastic scene layout generation from a label set"):
  with members:
    - "generating multiple objects at spatially distinct locations"
    - "object driven text to image synthesis via adversarial training"
    - "layoutgan generating graphic layouts with wireframe discriminators"
    - "semantic image synthesis with spatially adaptive normalization"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "st gan spatial transformer generative adversarial networks for image compositing"
    - "grains generative recursive autoencoders for indoor scenes"
    - "context aware synthesis and placement of object instances"

Connected through hyperedge HE-2 (induced by source paper "neural design network graphic layout generation with constraints"):
  with members:
    - "layoutgan generating graphic layouts with wireframe discriminators"
    - "generating diverse high fidelity images with vq vae 2"
    - "layoutvae stochastic scene layout generation from a label set"
    - "dancing to music"
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "graph r cnn for scene graph generation"
    - "a style based generator architecture for generative adversarial networks"
    - "image generation from scene graphs"

Connected through hyperedge HE-3 (induced by source paper "temporally coherent video harmonization using adversarial networks"):
  with members:
    - "mhp vos multiple hypotheses propagation for video object segmentation"
    - "adversarial spatio temporal learning for video deblurring"
    - "yotube searching action proposal via recurrent and static regression networks"
    - "cnn in mrf video object segmentation via inference in a cnn based higher order spatio temporal mrf"
    - "unsupervised image to image translation with stacked cycle consistent adversarial networks"
    - "dual motion gan for future flow embedded video prediction"
    - "the 2017 davis challenge on video object segmentation"
    - "lucid data dreaming for video object segmentation"

Connected through hyperedge HE-4 (induced by source paper "storygan a sequential conditional gan for story visualization"):
  with members:
    - "chatpainter improving text to image generation using dialogue"
    - "learning to forecast and refine residual motion for image to video generation"
    - "every smile is unique landmark guided diverse smile generation"
    - "deep video generation prediction and completion of human action sequences"
    - "hierarchically structured reinforcement learning for topically coherent visual story generation"
    - "tell draw and repeat generating and modifying images based on continual linguistic instruction"
    - "probabilistic video generation using holistic attribute control"
    - "sequential attention gan for interactive image editing via dialogue"

Connected through hyperedge HE-5 (induced by source paper "text2scene generating compositional scenes from textual descriptions"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "chatpainter improving text to image generation using dialogue"
    - "photographic text to image synthesis with a hierarchically nested adversarial network"
    - "semi parametric image synthesis"
    - "image generation from scene graphs"
    - "attngan fine grained text to image generation with attentional generative adversarial networks"
    - "obj2text generating visually descriptive language from object layouts"
    - "photographic image synthesis with cascaded refinement 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)