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: "a dataset to evaluate the representations learned by video prediction models"
  Abstract: We present a parameterized synthetic dataset called Moving Symbols to support the objective study of video prediction networks. Using several instantiations of the dataset in which variation is explicitly controlled, we highlight issues in an existing state-of-the-art approach and propose the use of a performance metric with greater semantic meaning to improve experimental interpretability. Our dataset provides canonical test cases that will help the community better understand, and eventually improve, the representations learned by such networks in the future. Code is available at this https URL .

Connected through hyperedge HE-1 (induced by source paper "video extrapolation with an invertible linear embedding"):
  with members:
    - "ffjord free form continuous dynamics for scalable reversible generative models"
    - "recurrent environment simulators"
    - "robust locally linear controllable embedding"
    - "density estimation using real nvp"
    - "deep predictive coding networks for video prediction and unsupervised learning"
    - "embed to control a locally linear latent dynamics model for control from raw images"
    - "action conditional video prediction using deep networks in atari games"
    - "deep multi scale video prediction beyond mean square error"

Connected through hyperedge HE-2 (induced by source paper "object localization with a weakly supervised capsnet"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "stochastic adversarial video prediction"
    - "semi convolutional operators for instance segmentation"
    - "decomposing motion and content for natural video sequence prediction"
    - "stochastic variational video prediction"
    - "dynamic routing between capsules"
    - "video imagination from a single image with transformation generation"
    - "unsupervised learning for physical interaction through 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)