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: "video prediction for precipitation nowcasting"
  Abstract: Video prediction, which aims to synthesize new consecutive frames subsequent to an existing video. However, its performance suffers from uncertainty of the future. As a potential weather application for video prediction, short time precipitation nowcasting is a more challenging task than other ones as its uncertainty is highly influenced by temperature, atmospheric, wind, humidity and such like. To address this issue, we propose a star-bridge neural network (StarBriNet). Specifically, we first construct a simple yet effective star-shape information bridge for RNN to transfer features across time-steps. We also propose a novel loss function designed for precipitaion nowcasting task. Furthermore, we utilize group normalization to refine the predictive performance of our network. Experiments in a Moving-Digital dataset and a weather predicting dataset demonstrate that our model outperforms the state-of-the-art algorithms for video prediction and precipitation nowcasting, achieving satisfied weather forecasting performance.

Connected through hyperedge HE-1 (induced by source paper "deep variational luenberger type observer for stochastic video prediction"):
  with members:
    - "stochastic prediction of multi agent interactions from partial observations"
    - "disentangling factors of variation using few labels"
    - "spatial broadcast decoder a simple architecture for learning disentangled representations in vaes"
    - "conditional flow variational autoencoders for structured sequence prediction"
    - "unsupervised discovery of parts structure and dynamics"
    - "recurrent kalman networks factorized inference in high dimensional deep feature spaces"
    - "weakly and semi supervised human body part parsing via pose guided knowledge transfer"
    - "state space lstm models with particle mcmc inference"

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)