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: "contextvp fully context aware video prediction"
  Abstract: Video prediction models based on convolutional networks, recurrent networks, and their combinations often result in blurry predictions. We identify an important contributing factor for imprecise predictions that has not been studied adequately in the literature: blind spots, i.e., lack of access to all relevant past information for accurately predicting the future. To address this issue, we introduce a fully context-aware architecture that captures the entire available past context for each pixel using Parallel Multi-Dimensional LSTM units and aggregates it using blending units. Our model outperforms a strong baseline network of 20 recurrent convolutional layers and yields state-of-the-art performance for next step prediction on three challenging real-world video datasets: Human 3.6M, Caltech Pedestrian, and UCF-101. Moreover, it does so with fewer parameters than several recently proposed models, and does not rely on deep convolutional networks, multi-scale architectures, separation of background and foreground modeling, motion flow learning, or adversarial training. These results highlight that full awareness of past context is of crucial importance for video prediction.

Connected through hyperedge HE-1 (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-2 (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-3 (induced by source paper "arma nets expanding receptive field for dense prediction"):
  with members:
    - "scale aware trident networks for object detection"
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "deformable convnets v2 more deformable better results"
    - "smoothed dilated convolutions for improved dense prediction"
    - "active convolution learning the shape of convolution for image classification"
    - "learning affinity via spatial propagation networks"
    - "deformable convolutional networks"
    - "dilated residual networks"

Connected through hyperedge HE-4 (induced by source paper "photo realistic video prediction on natural videos of largely changing frames"):
  with members:
    - "disentangling propagation and generation for video prediction"
    - "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"
    - "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 "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-6 (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"
    - "progressive growing of gans for improved quality stability and variation"
    - "stochastic variational video prediction"
    - "unsupervised learning of disentangled representations from video"
    - "photorealistic style transfer with screened poisson equation"

Connected through hyperedge HE-7 (induced by source paper "segmenting the future"):
  with members:
    - "predicting future instance segmentations by forecasting convolutional features"
    - "action agnostic human pose forecasting"
    - "social gan socially acceptable trajectories with generative adversarial networks"
    - "bayesian prediction of future street scenes using synthetic likelihoods"
    - "context encoding for semantic segmentation"
    - "d3d distilled 3d networks for video action recognition"
    - "future semantic segmentation with convolutional lstm"
    - "learning spatio temporal representation with pseudo 3d residual networks"

Connected through hyperedge HE-8 (induced by source paper "improved conditional vrnns for video prediction"):
  with members:
    - "videoflow a flow based generative model for video"
    - "stochastic adversarial video prediction"
    - "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"
    - "unsupervised representation learning by predicting image rotations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "self supervised visual planning with temporal skip connections"

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)