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: "video imagination from a single image with transformation generation"
  Abstract: In this work, we focus on a challenging task: synthesizing multiple imaginary videos given a single image. Major problems come from high dimensionality of pixel space and the ambiguity of potential motions. To overcome those problems, we propose a new framework that produce imaginary videos by transformation generation. The generated transformations are applied to the original image in a novel volumetric merge network to reconstruct frames in imaginary video. Through sampling different latent variables, our method can output different imaginary video samples. The framework is trained in an adversarial way with unsupervised learning. For evaluation, we propose a new assessment metric $RIQA$. In experiments, we test on 3 datasets varying from synthetic data to natural scene. Our framework achieves promising performance in image quality assessment. The visual inspection indicates that it can successfully generate diverse five-frame videos in acceptable perceptual quality.

Connected through hyperedge HE-1 (induced by source paper "learning predictive models from observation and interaction"):
  with members:
    - "experience embedded visual foresight"
    - "imitation learning from video by leveraging proprioception"
    - "improved conditional vrnns for video prediction"
    - "learning robotic manipulation through visual planning and acting"
    - "model based reinforcement learning for atari"
    - "nuscenes a multimodal dataset for autonomous driving"
    - "adversarial imitation learning from incomplete demonstrations"
    - "improvisation through physical understanding using novel objects as tools with visual foresight"

Connected through hyperedge HE-2 (induced by source paper "from here to there video inbetweening using direct 3d convolutions"):
  with members:
    - "hierarchical long term video prediction without supervision"
    - "phasenet for video frame interpolation"
    - "a two stream variational adversarial network for video generation"
    - "futuregan anticipating the future frames of video sequences using spatio temporal 3d convolutions in progressively growing gans"
    - "stochastic adversarial video prediction"
    - "style separation and synthesis via generative adversarial networks"
    - "stochastic video generation with a learned prior"
    - "towards accurate generative models of video a new metric challenges"

Connected through hyperedge HE-3 (induced by source paper "compositional video prediction"):
  with members:
    - "mt vae learning motion transformations to generate multimodal human dynamics"
    - "shapestacks learning vision based physical intuition for generalised object stacking"
    - "stochastic video generation with a learned prior"
    - "relational inductive biases deep learning and graph networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "interpretable intuitive physics model"
    - "learning to decompose and disentangle representations for video prediction"
    - "decomposing motion and content for natural video sequence prediction"

Connected through hyperedge HE-4 (induced by source paper "inferring dynamic representations of facial actions from a still image"):
  with members:
    - "deep affect prediction in the wild aff wild database and challenge deep architectures and beyond"
    - "a functional regression approach to facial landmark tracking"
    - "action anticipation by predicting future dynamic images"
    - "ganimation anatomically aware facial animation from a single image"
    - "joint action unit localisation and intensity estimation through heatmap regression"
    - "deep structured learning for facial action unit intensity estimation"
    - "facial expression recognition using enhanced deep 3d convolutional neural networks"
    - "deepcoder semi parametric variational autoencoders for automatic facial action coding"

Connected through hyperedge HE-5 (induced by source paper "unsupervised bi directional flow based video generation from one snapshot"):
  with members:
    - "video enhancement with task oriented flow"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "flow grounded spatial temporal video prediction from still images"
    - "dual motion gan for future flow embedded video prediction"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "mocogan decomposing motion and content for video generation"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "unsupervised learning of long term motion dynamics for videos"

Connected through hyperedge HE-6 (induced by source paper "stochastic adversarial video prediction"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "stochastic video generation with a learned prior"
    - "self supervised visual planning with temporal skip connections"
    - "decomposing motion and content for natural video sequence prediction"
    - "se3 pose nets structured deep dynamics models for visuomotor planning and control"
    - "stochastic variational video prediction"
    - "unsupervised learning of disentangled representations from video"
    - "recurrent environment simulators"

Connected through hyperedge HE-7 (induced by source paper "futuregan anticipating the future frames of video sequences using spatio temporal 3d convolutions in progressively growing gans"):
  with members:
    - "stochastic video generation with a learned prior"
    - "dual motion gan for future flow embedded video prediction"
    - "decomposing motion and content for natural video sequence prediction"
    - "learning to generate long term future via hierarchical prediction"
    - "folded recurrent neural networks for future 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-8 (induced by source paper "object localization with a weakly supervised capsnet"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "stochastic adversarial video prediction"
    - "a dataset to evaluate the representations learned by video prediction models"
    - "semi convolutional operators for instance segmentation"
    - "decomposing motion and content for natural video sequence prediction"
    - "stochastic variational video prediction"
    - "dynamic routing between capsules"
    - "unsupervised learning for physical interaction through video prediction"

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)