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 "deep variational luenberger type observer for stochastic video prediction"):
  Abstract: Considering the inherent stochasticity and uncertainty, predicting future video frames is exceptionally challenging. In this work, we study the problem of video prediction by combining interpretability of stochastic state space models and representation learning of deep neural networks. Our model builds upon an variational encoder which transforms the input video into a latent feature space and a Luenberger-type observer which captures the dynamic evolution of the latent features. This enables the decomposition of videos into static features and dynamics in an unsupervised manner. By deriving the stability theory of the nonlinear Luenberger-type observer, the hidden states in the feature space become insensitive with respect to the initial values, which improves the robustness of the overall model. Furthermore, the variational lower bound on the data log-likelihood can be derived to obtain the tractable posterior prediction distribution based on the variational principle. Finally, the experiments such as the Bouncing Balls dataset and the Pendulum dataset are provided to demonstrate the proposed model outperforms concurrent works.

Includes members:
  Member "video prediction for precipitation nowcasting":
    also connected through other hyperedges:
      (none)
  Member "stochastic prediction of multi agent interactions from partial observations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "relational state space model for stochastic multi object systems")
  Member "disentangling factors of variation using few labels":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "low resource knowledge grounded dialogue generation")
  Member "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "high fidelity synthesis with disentangled representation")
  Member "conditional flow variational autoencoders for structured sequence prediction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "latent normalizing flows for many to many cross domain mappings")
  Member "unsupervised discovery of parts structure and dynamics":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "entity abstraction in visual model based reinforcement learning")
  Member "recurrent kalman networks factorized inference in high dimensional deep feature spaces":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "disentangling physical dynamics from unknown factors for unsupervised video prediction")
  Member "weakly and semi supervised human body part parsing via pose guided knowledge transfer":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "hierarchical human parsing with typed part relation reasoning")

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)