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: "deep learning for precipitation nowcasting a benchmark and a new model"
  Abstract: With the goal of making high-resolution forecasts of regional rainfall, precipitation nowcasting has become an important and fundamental technology underlying various public services ranging from rainstorm warnings to flight safety. Recently, the Convolutional LSTM (ConvLSTM) model has been shown to outperform traditional optical flow based methods for precipitation nowcasting, suggesting that deep learning models have a huge potential for solving the problem. However, the convolutional recurrence structure in ConvLSTM-based models is location-invariant while natural motion and transformation (e.g., rotation) are location-variant in general. Furthermore, since deep-learning-based precipitation nowcasting is a newly emerging area, clear evaluation protocols have not yet been established. To address these problems, we propose both a new model and a benchmark for precipitation nowcasting. Specifically, we go beyond ConvLSTM and propose the Trajectory GRU (TrajGRU) model that can actively learn the location-variant structure for recurrent connections. Besides, we provide a benchmark that includes a real-world large-scale dataset from the Hong Kong Observatory, a new training loss, and a comprehensive evaluation protocol to facilitate future research and gauge the state of the art.

Connected through hyperedge HE-1 (induced by source paper "transformation based adversarial video prediction on large scale data"):
  with members:
    - "a short note on the kinetics 700 human action dataset"
    - "large scale adversarial representation learning"
    - "high fidelity speech synthesis with adversarial networks"
    - "adversarial video generation on complex datasets"
    - "scaling autoregressive video models"
    - "object oriented dynamics predictor"
    - "tganv2 efficient training of large models for video generation with multiple subsampling layers"
    - "sequential attend infer repeat generative modelling of moving objects"

Connected through hyperedge HE-2 (induced by source paper "deep variational luenberger type observer for stochastic video prediction"):
  with members:
    - "video prediction for precipitation nowcasting"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "interpretable crowd flow prediction with spatial temporal self attention"):
  with members:
    - "deep bidirectional and unidirectional lstm recurrent neural network for network wide traffic speed prediction"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "attention is all you need"
    - "short term forecasting of passenger demand under on demand ride services a spatio temporal deep learning approach"
    - "deep convolutional networks on graph structured data"
    - "deep residual learning for image recognition"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

Connected through hyperedge HE-4 (induced by source paper "deep learning for spatio temporal data mining a survey"):
  with members:
    - "t gcn a temporal graph convolutional network for traffic prediction"
    - "learning from multiple cities a meta learning approach for spatial temporal prediction"
    - "a comprehensive survey on graph neural networks"
    - "inferring transportation modes from gps trajectories using a convolutional neural network"
    - "where to go next a spatio temporal lstm model for next poi recommendation"
    - "forecasting transportation network speed using deep capsule networks with nested lstm models"
    - "a hybrid method for traffic flow forecasting using multimodal deep learning"
    - "deep sequence learning with auxiliary information for traffic prediction"

Connected through hyperedge HE-5 (induced by source paper "temporally consistent depth prediction with flow guided memory units"):
  with members:
    - "deep ordinal regression network for monocular depth estimation"
    - "learning blind video temporal consistency"
    - "geonet unsupervised learning of dense depth optical flow and camera pose"
    - "unsupervised learning of depth and ego motion from video"
    - "learning depth from monocular videos using direct methods"
    - "deformable convolutional networks"
    - "pwc net cnns for optical flow using pyramid warping and cost volume"
    - "semantic video cnns through representation warping"

Connected through hyperedge HE-6 (induced by source paper "spatio temporal video re localization by warp lstm"):
  with members:
    - "adversarial spatio temporal learning for video deblurring"
    - "bidirectional attentive fusion with context gating for dense video captioning"
    - "a better baseline for ava"
    - "video re localization"
    - "regularizing rnns for caption generation by reconstructing the past with the present"
    - "actor centric relation network"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "reconstruction network for video captioning"

Connected through hyperedge HE-7 (induced by source paper "progress regression rnn for online spatial temporal action localization in unconstrained videos"):
  with members:
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "amtnet action micro tube regression by end to end trainable deep architecture"
    - "predictive corrective networks for action detection"
    - "action tubelet detector for spatio temporal action localization"
    - "temporal action localization by structured maximal sums"
    - "chained multi stream networks exploiting pose motion and appearance for action classification and detection"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"

Connected through hyperedge HE-8 (induced by source paper "attention driven tree structured convolutional lstm for high dimensional data understanding"):
  with members:
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function"
    - "residual attention network for image classification"
    - "unsupervised learning for physical interaction through video prediction"
    - "ntu rgb d a large scale dataset for 3d human activity analysis"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "hierarchical question image co attention for visual question answering"

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)