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: "extremeweather a large scale climate dataset for semi supervised detection localization and understanding of extreme weather events"
  Abstract: Then detection and identification of extreme weather events in large-scale climate simulations is an important problem for risk management, informing governmental policy decisions and advancing our basic understanding of the climate system. Recent work has shown that fully supervised convolutional neural networks (CNNs) can yield acceptable accuracy for classifying well-known types of extreme weather events when large amounts of labeled data are available. However, many different types of spatially localized climate patterns are of interest including hurricanes, extra-tropical cyclones, weather fronts, and blocking events among others. Existing labeled data for these patterns can be incomplete in various ways, such as covering only certain years or geographic areas and having false negatives. This type of climate data therefore poses a number of interesting machine learning challenges. We present a multichannel spatiotemporal CNN architecture for semi-supervised bounding box prediction and exploratory data analysis. We demonstrate that our approach is able to leverage temporal information and unlabeled data to improve the localization of extreme weather events. Further, we explore the representations learned by our model in order to better understand this important data. We present a dataset, ExtremeWeather, to encourage machine learning research in this area and to help facilitate further work in understanding and mitigating the effects of climate change. The dataset is available at extremeweatherdataset.github.io and the code is available at this https URL

Connected through hyperedge HE-1 (induced by source paper "tackling climate change with machine learning"):
  with members:
    - "pvnet a lrcn architecture for spatio temporal photovoltaic powerforecasting from numerical weather prediction"
    - "truck traffic monitoring with satellite images"
    - "artificial intelligence for social good"
    - "efficient multi objective optimization through population based parallel surrogate search"
    - "agriculture commodity arrival prediction using remote sensing data insights and beyond"
    - "decentralized flood forecasting using deep neural networks"
    - "machine vision for natural gas methane emissions detection using an infrared camera"
    - "learning optimal solutions for extremely fast ac optimal power flow"

Connected through hyperedge HE-2 (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-3 (induced by source paper "spherical cnns on unstructured grids"):
  with members:
    - "deep neural networks motivated by partial differential equations"
    - "dynamic graph cnn for learning on point clouds"
    - "im2pano3d extrapolating 360 structure and semantics beyond the field of view"
    - "pointnet deep hierarchical feature learning on point sets in a metric space"
    - "learning spherical convolution for fast features from 360 deg imagery"
    - "segcloud semantic segmentation of 3d point clouds"
    - "learning so 3 equivariant representations with spherical cnns"
    - "geometric deep learning going beyond euclidean data"

Connected through hyperedge HE-4 (induced by source paper "probabilistic forecasting of sensory data with generative adversarial networks forgan"):
  with members:
    - "phaselink a deep learning approach to seismic phase association"
    - "predicting inpatient discharge prioritization with electronic health records"
    - "deepdownscale a deep learning strategy for high resolution weather forecast"
    - "anomaly detection with generative adversarial networks for multivariate time series"
    - "voice impersonation using generative adversarial networks"
    - "cred a deep residual network of convolutional and recurrent units for earthquake signal detection"
    - "svsgan singing voice separation via generative adversarial network"
    - "adversarial learning for neural dialogue generation"

Connected through hyperedge HE-5 (induced by source paper "improved adversarial robustness by reducing open space risk via tent activations"):
  with members:
    - "out of distribution detection using an ensemble of self supervised leave out classifiers"
    - "evidential deep learning to quantify classification uncertainty"
    - "efficient formal safety analysis of neural networks"
    - "self normalizing neural networks"
    - "sparse representation based open set recognition"
    - "noisy activation functions"
    - "wide residual networks"
    - "towards evaluating the robustness of neural networks"

Connected through hyperedge HE-6 (induced by source paper "stconvs2s spatiotemporal convolutional sequence to sequence network for weather forecasting"):
  with members:
    - "recurrent convolutions for causal 3d cnns"
    - "spatio temporal graph convolutional networks a deep learning framework for traffic forecasting"
    - "an empirical evaluation of generic convolutional and recurrent networks for sequence modeling"
    - "prediction of sea surface temperature using long short term memory"
    - "a closer look at spatiotemporal convolutions for action recognition"
    - "attention is all you need"
    - "modeling long and short term temporal patterns with deep neural networks"
    - "convolutional sequence to sequence learning"

Connected through hyperedge HE-7 (induced by source paper "predicting weather uncertainty with deep convnets"):
  with members:
    - "tackling climate change with machine learning"
    - "improving strong scaling of cnn training by exploiting finer grained parallelism"
    - "pipe sgd a decentralized pipelined sgd framework for distributed deep net training"
    - "gpipe efficient training of giant neural networks using pipeline parallelism"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "deep learning for precipitation nowcasting a benchmark and a new model"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-8 (induced by source paper "distributed deep learning for precipitation nowcasting"):
  with members:
    - "ampnet asynchronous model parallel training for dynamic neural networks"
    - "horovod fast and easy distributed deep learning in tensorflow"
    - "imagenet training in minutes"
    - "a survey on deep learning in medical image analysis"
    - "accurate large minibatch sgd training imagenet in 1 hour"
    - "application of deep convolutional neural networks for detecting extreme weather in climate datasets"
    - "u net convolutional networks for biomedical image segmentation"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

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)