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: "one step time dependent future video frame prediction with a convolutional encoder decoder neural network"
  Abstract: There is an inherent need for machines to have a notion of how entities within their environment behave and to anticipate changes in the near future. In this work, we focus on anticipating future appearance given the current frame of a video. Typical methods are used either to predict the next frame of a video or to predict future optical flow or trajectories based on a single video frame. This work presents an experiment on stretching the ability of CNNs to predict %not the next frame, but an anticipation of appearance at an arbitrarily given future time. We condition our predicted video frames on a continuous time variable that allows us to anticipate future frames at a given temporal distance, directly from the current input video frame. We show that CNNs can learn an intrinsic representation of typical appearance changes over time and successfully generate realistic predictions in one step - at a deliberate time difference in the near future. The method is evaluated on the KTH human actions dataset and compared to a baseline consisting of an analogous CNN architecture that is not time-aware.

Connected through hyperedge HE-1 (induced by source paper "pisep 2 pseudo image sequence evolution based 3d pose prediction"):
  with members:
    - "frequency domain transformer networks for video prediction"
    - "human action recognition and prediction a survey"
    - "predrnn towards a resolution of the deep in time dilemma in spatiotemporal predictive learning"
    - "deep video generation prediction and completion of human action sequences"
    - "stochastic adversarial video prediction"
    - "co occurrence feature learning from skeleton data for action recognition and detection with hierarchical aggregation"
    - "timeception for complex action recognition"
    - "long term human motion prediction by modeling motion context and enhancing motion dynamic"

Connected through hyperedge HE-2 (induced by source paper "overcoming limitations of mixture density networks a sampling and fitting framework for multimodal future prediction"):
  with members:
    - "hierarchical long term video prediction without supervision"
    - "multimodal trajectory predictions for autonomous driving using deep convolutional networks"
    - "predicting future instance segmentations by forecasting convolutional features"
    - "egocentric vision based future vehicle localization for intelligent driving assistance systems"
    - "multimodal interaction aware motion prediction for autonomous street crossing"
    - "learning what you can do before doing anything"
    - "flow grounded spatial temporal video prediction from still images"
    - "action anticipation by predicting future dynamic images"

Connected through hyperedge HE-3 (induced by source paper "single level feature to feature forecasting with deformable convolutions"):
  with members:
    - "panoptic segmentation"
    - "in defense of pre trained imagenet architectures for real time semantic segmentation of road driving images"
    - "efficient ladder style densenets for semantic segmentation of large images"
    - "predicting future instance segmentations by forecasting convolutional features"
    - "recurrent flow guided semantic forecasting"
    - "deformable convnets v2 more deformable better results"
    - "bayesian prediction of future street scenes using synthetic likelihoods"
    - "predicting deeper into the future of semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "learning wear patterns on footwear outsoles using convolutional neural networks"):
  with members:
    - "generative image inpainting with contextual attention"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "unsupervised learning for physical interaction through video prediction"
    - "context encoders feature learning by inpainting"
    - "multi view 3d models from single images with a convolutional network"
    - "compression artifacts reduction by a deep convolutional network"
    - "deep multi scale video prediction beyond mean square error"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-5 (induced by source paper "when will you do what anticipating temporal occurrences of activities"):
  with members:
    - "dual motion gan for future flow embedded video prediction"
    - "predicting deeper into the future of semantic segmentation"
    - "weakly supervised action learning with rnn based fine to coarse modeling"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "learning to generate long term future via hierarchical prediction"
    - "anticipating daily intention using on wrist motion triggered sensing"
    - "encouraging lstms to anticipate actions very early"
    - "visual forecasting by imitating dynamics in natural sequences"

Connected through hyperedge HE-6 (induced by source paper "video generation from text"):
  with members:
    - "decomposing motion and content for natural video sequence prediction"
    - "forecasting human dynamics from static images"
    - "the kinetics human action video dataset"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "mocogan decomposing motion and content for video generation"
    - "video imagination from a single image with transformation generation"
    - "transformation based models of video sequences"
    - "the pose knows video forecasting by generating pose futures"

Connected through hyperedge HE-7 (induced by source paper "folded recurrent neural networks for future video prediction"):
  with members:
    - "dual motion gan for future flow embedded video prediction"
    - "self supervised visual planning with temporal skip connections"
    - "recurrent ladder networks"
    - "decomposing motion and content for natural video sequence prediction"
    - "hybrid learning of optical flow and next frame prediction to boost optical flow in the wild"
    - "stochastic variational video prediction"
    - "unsupervised learning of disentangled representations from video"
    - "video frame synthesis using deep voxel flow"

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)