human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "disentangling physical dynamics from unknown factors for unsupervised video prediction"):
  Abstract: Leveraging physical knowledge described by partial differential equations (PDEs) is an appealing way to improve unsupervised video prediction methods. Since physics is too restrictive for describing the full visual content of generic videos, we introduce PhyDNet, a two-branch deep architecture, which explicitly disentangles PDE dynamics from unknown complementary information. A second contribution is to propose a new recurrent physical cell (PhyCell), inspired from data assimilation techniques, for performing PDE-constrained prediction in latent space. Extensive experiments conducted on four various datasets show the ability of PhyDNet to outperform state-of-the-art methods. Ablation studies also highlight the important gain brought out by both disentanglement and PDE-constrained prediction. Finally, we show that PhyDNet presents interesting features for dealing with missing data and long-term forecasting.

Includes members:
  Member "differentiable physics informed graph networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "hamiltonian graph networks with ode integrators")
  Member "improved conditional vrnns for video prediction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "stochastic latent residual video prediction")
  Member "neural jump stochastic differential equations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dissecting neural odes")
  Member "unsupervised learning of object structure and dynamics from videos":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "stochastic latent residual video prediction")
  Member "compositional video prediction":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "object centric forward modeling for model predictive control")
  Member "pde net 2 0 learning pdes from data with a numeric symbolic hybrid deep network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using deep learning to extend the range of air pollution monitoring and forecasting")
  Member "recurrent kalman networks factorized inference in high dimensional deep feature spaces":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "deep variational luenberger type observer for stochastic video prediction")
  Member "memory in memory a predictive neural network for learning higher order non stationarity from spatiotemporal dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "spatiotemporal tile based attention guided lstms for traffic video prediction")

Question: Please tell me which class the source paper 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)