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: "disentangling propagation and generation for video prediction"
  Abstract: Learning to predict future video frames is a challenging task. Recent approaches for natural scenes directly predict pixels via inferring appearance flow and using flow-guided warping. Such models excel when motion estimates are accurate, but the motion may be ambiguous or erroneous in many real scenes. When scene motion exposes new regions of the scene, motion-based prediction yields poor results. However, learning to predict novel pixels directly can also require a prohibitive amount of training. In this work, we present a confidence-aware spatial-temporal context encoder for video prediction called Flow-Grounded Video Prediction (FGVP), in which motion propagation and novel pixel generation are first disentangled and then fused according to computed flow uncertainty map. For regions where motion-based prediction shows low-confidence, our model uses a conditional context encoder to hallucinate appropriate content. We test our methods on the standard CalTech Pedestrian dataset and the more challenging KITTI Flow dataset of larger motions and occlusions. Our methods produce both sharp and natural predictions compared to previous works, achieving the state-of-the-art performance on both datasets.

Connected through hyperedge HE-1 (induced by source paper "transformation based adversarial video prediction on large scale data"):
  with members:
    - "a short note on the kinetics 700 human action dataset"
    - "large scale adversarial representation learning"
    - "high fidelity speech synthesis with adversarial networks"
    - "adversarial video generation on complex datasets"
    - "scaling autoregressive video models"
    - "object oriented dynamics predictor"
    - "tganv2 efficient training of large models for video generation with multiple subsampling layers"
    - "sequential attend infer repeat generative modelling of moving objects"

Connected through hyperedge HE-2 (induced by source paper "stochastic latent residual video prediction"):
  with members:
    - "videoflow a conditional flow based model for stochastic video generation"
    - "improved conditional vrnns for video prediction"
    - "cubic lstms for video prediction"
    - "unsupervised learning of object structure and dynamics from videos"
    - "scaling autoregressive video models"
    - "video to video synthesis"
    - "hierarchical long term video prediction without supervision"
    - "learning latent dynamics for planning from pixels"

Connected through hyperedge HE-3 (induced by source paper "disentangling physical dynamics from unknown factors for unsupervised video prediction"):
  with members:
    - "differentiable physics informed graph networks"
    - "improved conditional vrnns for video prediction"
    - "neural jump stochastic differential equations"
    - "unsupervised learning of object structure and dynamics from videos"
    - "compositional video prediction"
    - "pde net 2 0 learning pdes from data with a numeric symbolic hybrid deep network"
    - "recurrent kalman networks factorized inference in high dimensional deep feature spaces"
    - "memory in memory a predictive neural network for learning higher order non stationarity from spatiotemporal dynamics"

Connected through hyperedge HE-4 (induced by source paper "photo realistic video prediction on natural videos of largely changing frames"):
  with members:
    - "spectral normalization for generative adversarial networks"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "dual motion gan for future flow embedded video prediction"
    - "contextvp fully context aware video prediction"
    - "learning to generate long term future via hierarchical prediction"
    - "video frame synthesis using deep voxel flow"
    - "unsupervised learning for physical interaction through video prediction"
    - "perceptual losses for real time style transfer and super resolution"

Connected through hyperedge HE-5 (induced by source paper "enhancing traffic scene predictions with generative adversarial networks"):
  with members:
    - "sequential attend infer repeat generative modelling of moving objects"
    - "learning to forecast and refine residual motion for image to video generation"
    - "reduced gate convolutional lstm using predictive coding for spatiotemporal prediction"
    - "yolov3 an incremental improvement"
    - "futuregan anticipating the future frames of video sequences using spatio temporal 3d convolutions in progressively growing gans"
    - "novel video prediction for large scale scene using optical flow"
    - "bayesian prediction of future street scenes using synthetic likelihoods"
    - "dyan a dynamical atoms based network for 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)