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: "learning temporal transformations from time lapse videos"
  Abstract: Based on life-long observations of physical, chemical, and biologic phenomena in the natural world, humans can often easily picture in their minds what an object will look like in the future. But, what about computers? In this paper, we learn computational models of object transformations from time-lapse videos. In particular, we explore the use of generative models to create depictions of objects at future times. These models explore several different prediction tasks: generating a future state given a single depiction of an object, generating a future state given two depictions of an object at different times, and generating future states recursively in a recurrent framework. We provide both qualitative and quantitative evaluations of the generated results, and also conduct a human evaluation to compare variations of our models.

Connected through hyperedge HE-1 (induced by source paper "painting many pasts synthesizing time lapse videos of paintings"):
  with members:
    - "lpaintb learning to paint from self supervision"
    - "visual deprojection probabilistic recovery of collapsed dimensions"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "context aware synthesis for video frame interpolation"
    - "synthesizing programs for images using reinforced adversarial learning"
    - "learning to generate long term future via hierarchical prediction"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "latent constraints learning to generate conditionally from unconditional generative models"

Connected through hyperedge HE-2 (induced by source paper "harmonizing maximum likelihood with gans for multimodal conditional generation"):
  with members:
    - "on the convergence of adam and beyond"
    - "video to video synthesis"
    - "to learn image super resolution use a gan to learn how to do image degradation first"
    - "spg net segmentation prediction and guidance network for image inpainting"
    - "codeslam learning a compact optimisable representation for dense visual slam"
    - "video prediction with appearance and motion conditions"
    - "pedestrian synthesis gan generating pedestrian data in real scene and beyond"
    - "image inpainting using block wise procedural training with annealed adversarial counterpart"

Connected through hyperedge HE-3 (induced by source paper "animating landscape self supervised learning of decoupled motion and appearance for single image video synthesis"):
  with members:
    - "manipulating attributes of natural scenes via hallucination"
    - "flow grounded spatial temporal video prediction from still images"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "personalized cinemagraphs using semantic understanding and collaborative learning"
    - "contextvp fully context aware video prediction"
    - "progressive growing of gans for improved quality stability and variation"
    - "stochastic variational video prediction"
    - "unsupervised learning of disentangled representations from video"

Connected through hyperedge HE-4 (induced by source paper "end to end time lapse video synthesis from a single outdoor image"):
  with members:
    - "video to video synthesis"
    - "recycle gan unsupervised video retargeting"
    - "learning intrinsic image decomposition from watching the world"
    - "a closed form solution to photorealistic image stylization"
    - "deep sets"
    - "night to day image translation for retrieval based localization"
    - "drit diverse image to image translation via disentangled representations"
    - "decomposing motion and content for natural video sequence prediction"

Connected through hyperedge HE-5 (induced by source paper "cubic lstms for video prediction"):
  with members:
    - "self supervised visual planning with temporal skip connections"
    - "decomposing motion and content for natural video sequence prediction"
    - "motion prediction under multimodality with conditional stochastic networks"
    - "stochastic variational video prediction"
    - "unsupervised learning of disentangled representations from video"
    - "recurrent environment simulators"
    - "mocogan decomposing motion and content for video generation"
    - "learning to generate time lapse videos using multi stage dynamic generative adversarial networks"

Connected through hyperedge HE-6 (induced by source paper "dynamic facial expression generation on hilbert hypersphere with conditional wasserstein generative adversarial nets"):
  with members:
    - "deep covariance descriptors for facial expression recognition"
    - "learning to forecast and refine residual motion for image to video generation"
    - "every smile is unique landmark guided diverse smile generation"
    - "a novel geometric framework on gram matrix trajectories for human behavior understanding"
    - "controllable image to video translation a case study on facial expression generation"
    - "decomposing motion and content for natural video sequence prediction"
    - "learning to generate long term future via hierarchical prediction"
    - "fsrnet end to end learning face super resolution with facial priors"

Connected through hyperedge HE-7 (induced by source paper "coupled generative adversarial network for continuous fine grained action segmentation"):
  with members:
    - "stabilizing gan training with multiple random projections"
    - "task specific visual saliency prediction with memory augmented conditional generative adversarial networks"
    - "stabilizing training of generative adversarial networks through regularization"
    - "tricornet a hybrid temporal convolutional and recurrent network for video action segmentation"
    - "salgan visual saliency prediction with generative adversarial networks"
    - "infogail interpretable imitation learning from visual demonstrations"
    - "semi supervised learning with context conditional generative adversarial networks"
    - "two stream lstm a deep fusion framework for human action recognition"

Connected through hyperedge HE-8 (induced by source paper "fine grained action segmentation using the semi supervised action gan"):
  with members:
    - "robust visual tracking using multi frame multi feature joint modeling"
    - "coupled generative adversarial network for continuous fine grained action segmentation"
    - "tracking by prediction a deep generative model for mutli person localisation and tracking"
    - "multi level sequence gan for group activity recognition"
    - "discrimnet semi supervised action recognition from videos using generative adversarial networks"
    - "good semi supervised learning that requires a bad gan"
    - "tricornet a hybrid temporal convolutional and recurrent network for video action segmentation"
    - "infogail interpretable imitation learning from visual demonstrations"

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)