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: "dyan a dynamical atoms based network for video prediction"
  Abstract: The ability to anticipate the future is essential when making real time critical decisions, provides valuable information to understand dynamic natural scenes, and can help unsupervised video representation learning. State-of-art video prediction is based on LSTM recursive networks and/or generative adversarial network learning. These are complex architectures that need to learn large numbers of parameters, are potentially hard to train, slow to run, and may produce blurry predictions. In this paper, we introduce DYAN, a novel network with very few parameters and easy to train, which produces accurate, high quality frame predictions, significantly faster than previous approaches. DYAN owes its good qualities to its encoder and decoder, which are designed following concepts from systems identification theory and exploit the dynamics-based invariants of the data. Extensive experiments using several standard video datasets show that DYAN is superior generating frames and that it generalizes well across domains.

Connected through hyperedge HE-1 (induced by source paper "dynamic variational autoencoders for visual process modeling"):
  with members:
    - "alignment distances on systems of bags"
    - "two stream convolutional networks for dynamic texture synthesis"
    - "visual dynamics probabilistic future frame synthesis via cross convolutional networks"
    - "generating videos with scene dynamics"
    - "embed to control a locally linear latent dynamics model for control from raw images"
    - "deep predictive coding networks"

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 "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"
    - "disentangling propagation and generation for video prediction"
    - "novel video prediction for large scale scene using optical flow"
    - "bayesian prediction of future street scenes using synthetic likelihoods"

Connected through hyperedge HE-4 (induced by source paper "linearizing visual processes with convolutional variational autoencoders"):
  with members:
    - "alignment distances on systems of bags"
    - "two stream convolutional networks for dynamic texture synthesis"
    - "dynamical textures modeling via joint video dictionary learning"
    - "visual dynamics probabilistic future frame synthesis via cross convolutional networks"
    - "group equivariant convolutional networks"
    - "generating videos with scene dynamics"
    - "procedural generation of videos to train deep action recognition networks"
    - "a mathematical theory of deep convolutional neural networks for feature extraction"

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)