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: "stochastic variational video prediction"
  Abstract: Predicting the future in real-world settings, particularly from raw sensory observations such as images, is exceptionally challenging. Real-world events can be stochastic and unpredictable, and the high dimensionality and complexity of natural images requires the predictive model to build an intricate understanding of the natural world. Many existing methods tackle this problem by making simplifying assumptions about the environment. One common assumption is that the outcome is deterministic and there is only one plausible future. This can lead to low-quality predictions in real-world settings with stochastic dynamics. In this paper, we develop a stochastic variational video prediction (SV2P) method that predicts a different possible future for each sample of its latent variables. To the best of our knowledge, our model is the first to provide effective stochastic multi-frame prediction for real-world video. We demonstrate the capability of the proposed method in predicting detailed future frames of videos on multiple real-world datasets, both action-free and action-conditioned. We find that our proposed method produces substantially improved video predictions when compared to the same model without stochasticity, and to other stochastic video prediction methods. Our SV2P implementation will be open sourced upon publication.

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 "videoflow a conditional flow based model for stochastic video generation"):
  with members:
    - "waveglow a flow based generative network for speech synthesis"
    - "stochastic adversarial video prediction"
    - "flow grounded spatial temporal video prediction from still images"
    - "stochastic video generation with a learned prior"
    - "towards accurate generative models of video a new metric challenges"
    - "the unreasonable effectiveness of deep features as a perceptual metric"
    - "fast generation for convolutional autoregressive models"
    - "self supervised visual planning with temporal skip connections"

Connected through hyperedge HE-4 (induced by source paper "convolutional tensor train lstm for spatio temporal learning"):
  with members:
    - "a tensorized transformer for language modeling"
    - "efficient n dimensional convolutions via higher order factorization"
    - "robust deep networks with randomized tensor regression layers"
    - "scaling autoregressive video models"
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "stochastic adversarial video prediction"
    - "stochastic video generation with a learned prior"
    - "the unreasonable effectiveness of deep features as a perceptual metric"

Connected through hyperedge HE-5 (induced by source paper "first order motion model for image animation"):
  with members:
    - "textured neural avatars"
    - "few shot adversarial learning of realistic neural talking head models"
    - "dwnet dense warp based network for pose guided human video generation"
    - "dual generator generative adversarial networks for multi domain image to image translation"
    - "gesture to gesture translation in the wild via category independent conditional maps"
    - "3d guided fine grained face manipulation"
    - "video to video synthesis"
    - "learning to forecast and refine residual motion for image to video generation"

Connected through hyperedge HE-6 (induced by source paper "twostreamvan improving motion modeling in video generation"):
  with members:
    - "inferring semantic layout for hierarchical text to image synthesis"
    - "stochastic adversarial video prediction"
    - "probabilistic video generation using holistic attribute control"
    - "stochastic video generation with a learned prior"
    - "a style based generator architecture for generative adversarial networks"
    - "on convergence and stability of gans"
    - "unsupervised learning of disentangled representations from video"
    - "video frame interpolation via adaptive convolution"

Connected through hyperedge HE-7 (induced by source paper "deep variational luenberger type observer for stochastic video prediction"):
  with members:
    - "video prediction for precipitation nowcasting"
    - "stochastic prediction of multi agent interactions from partial observations"
    - "disentangling factors of variation using few labels"
    - "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
    - "conditional flow variational autoencoders for structured sequence prediction"
    - "unsupervised discovery of parts structure and dynamics"
    - "recurrent kalman networks factorized inference in high dimensional deep feature spaces"
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"

Connected through hyperedge HE-8 (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"

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)