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: "deepsd generating high resolution climate change projections through single image super resolution"
  Abstract: The impacts of climate change are felt by most critical systems, such as infrastructure, ecological systems, and power-plants. However, contemporary Earth System Models (ESM) are run at spatial resolutions too coarse for assessing effects this localized. Local scale projections can be obtained using statistical downscaling, a technique which uses historical climate observations to learn a low-resolution to high-resolution mapping. Depending on statistical modeling choices, downscaled projections have been shown to vary significantly terms of accuracy and reliability. The spatio-temporal nature of the climate system motivates the adaptation of super-resolution image processing techniques to statistical downscaling. In our work, we present DeepSD, a generalized stacked super resolution convolutional neural network (SRCNN) framework for statistical downscaling of climate variables. DeepSD augments SRCNN with multi-scale input channels to maximize predictability in statistical downscaling. We provide a comparison with Bias Correction Spatial Disaggregation as well as three Automated-Statistical Downscaling approaches in downscaling daily precipitation from 1 degree (~100km) to 1/8 degrees (~12.5km) over the Continental United States. Furthermore, a framework using the NASA Earth Exchange (NEX) platform is discussed for downscaling more than 20 ESM models with multiple emission scenarios.

Connected through hyperedge HE-1 (induced by source paper "fine grained urban flow inference"):
  with members:
    - "urbanfm inferring fine grained urban flows"
    - "image super resolution using very deep residual channel attention networks"
    - "a fully progressive approach to single image super resolution"
    - "deepdpm dynamic population mapping via deep neural network"
    - "gradnorm gradient normalization for adaptive loss balancing in deep multitask networks"
    - "fast and accurate image super resolution with deep laplacian pyramid networks"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "identity mappings in deep residual networks"

Connected through hyperedge HE-2 (induced by source paper "progressively growing generative adversarial networks for high resolution semantic segmentation of satellite images"):
  with members:
    - "temporary removal aerial imagery for roof segmentation a large scale dataset towards automatic mapping of buildings"
    - "automatic pixelwise object labeling for aerial imagery using stacked u nets"
    - "cactusnets layer applicability as a metric for transfer learning"
    - "overcoming missing and incomplete modalities with generative adversarial networks for building footprint segmentation"
    - "progressive growing of gans for improved quality stability and variation"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "adversarial deep structural networks for mammographic mass segmentation"
    - "semantic segmentation using adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "urbanfm inferring fine grained urban flows"):
  with members:
    - "cross city transfer learning for deep spatio temporal prediction"
    - "crowd flow prediction by deep spatio temporal transfer learning"
    - "enhanced deep residual networks for single image super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "identity mappings in deep residual networks"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "deep spatio temporal residual networks for citywide crowd flows prediction"
    - "accurate image super resolution using very deep convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "optical flow for intermediate frame interpolation of multispectral geostationary satellite data"):
  with members:
    - "super resolution of sentinel 2 images learning a globally applicable deep neural network"
    - "super slomo high quality estimation of multiple intermediate frames for video interpolation"
    - "video frame interpolation via adaptive convolution"
    - "video frame interpolation via adaptive separable convolution"
    - "spatio temporal video autoencoder with differentiable memory"
    - "u net convolutional networks for biomedical image segmentation"
    - "land use classification in remote sensing images by convolutional neural networks"
    - "deepsat a learning framework for satellite imagery"

Connected through hyperedge HE-5 (induced by source paper "deep learning emulation of multi angle implementation of atmospheric correction maiac"):
  with members:
    - "quantifying uncertainty in discrete continuous and skewed data with bayesian deep learning"
    - "cascaded recurrent neural networks for hyperspectral image classification"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "land use classification in remote sensing images by convolutional neural networks"
    - "transfer learning from deep features for remote sensing and poverty mapping"
    - "deepsat a learning framework for satellite imagery"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "quantifying uncertainty in discrete continuous and skewed data with bayesian deep learning"):
  with members:
    - "interactive medical image segmentation using deep learning with image specific fine tuning"
    - "what uncertainties do we need in bayesian deep learning for computer vision"
    - "extremeweather a large scale climate dataset for semi supervised detection localization and understanding of extreme weather events"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "bayesian segnet model uncertainty in deep convolutional encoder decoder architectures for scene understanding"
    - "accurate image super resolution using very deep convolutional networks"
    - "deepsat a learning framework for satellite imagery"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "deepdpm dynamic population mapping via deep neural network"):
  with members:
    - "end to end image super resolution via deep and shallow convolutional networks"
    - "a critical review of recurrent neural networks for sequence learning"
    - "deep residual learning for image recognition"
    - "deeply recursive convolutional network for image super resolution"
    - "deep networks for image super resolution with sparse prior"
    - "image super resolution using deep convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "machine learning for the geosciences challenges and opportunities"):
  with members:
    - "spatio temporal data mining a survey of problems and methods"
    - "extremeweather a large scale climate dataset for semi supervised detection localization and understanding of extreme weather events"
    - "label free supervision of neural networks with physics and domain knowledge"
    - "application of deep convolutional neural networks for detecting extreme weather in climate datasets"
    - "transfer learning from deep features for remote sensing and poverty mapping"
    - "deepsat a learning framework for satellite imagery"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "a model of inductive bias learning"

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)