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: "using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale"
  Abstract: Urban planning applications (energy audits, investment, etc.) require an understanding of built infrastructure and its environment, i.e., both low-level, physical features (amount of vegetation, building area and geometry etc.), as well as higher-level concepts such as land use classes (which encode expert understanding of socio-economic end uses). This kind of data is expensive and labor-intensive to obtain, which limits its availability (particularly in developing countries). We analyze patterns in land use in urban neighborhoods using large-scale satellite imagery data (which is available worldwide from third-party providers) and state-of-the-art computer vision techniques based on deep convolutional neural networks. For supervision, given the limited availability of standard benchmarks for remote-sensing data, we obtain ground truth land use class labels carefully sampled from open-source surveys, in particular the Urban Atlas land classification dataset of $20$ land use classes across $~300$ European cities. We use this data to train and compare deep architectures which have recently shown good performance on standard computer vision tasks (image classification and segmentation), including on geospatial data. Furthermore, we show that the deep representations extracted from satellite imagery of urban environments can be used to compare neighborhoods across several cities. We make our dataset available for other machine learning researchers to use for remote-sensing applications.

Connected through hyperedge HE-1 (induced by source paper "indexical cities articulating personal models of urban preference with geotagged data"):
  with members:
    - "deep learning the city quantifying urban perception at a global scale"
    - "very deep convolutional networks for large scale image recognition"
    - "computing with contextual numbers"

Connected through hyperedge HE-2 (induced by source paper "deep built structure counting in satellite imagery using attention based re weighting"):
  with members:
    - "xview objects in context in overhead imagery"
    - "exploring models and data for remote sensing image caption generation"
    - "patternnet a benchmark dataset for performance evaluation of remote sensing image retrieval"
    - "detecting and counting tiny faces"
    - "leveraging unlabeled data for crowd counting by learning to rank"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "switching convolutional neural network for crowd counting"
    - "decidenet counting varying density crowds through attention guided detection and density estimation"

Connected through hyperedge HE-3 (induced by source paper "apache spark accelerated deep learning inference for large scale satellite image analytics"):
  with members:
    - "building extraction at scale using convolutional neural network mapping of the united states"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "infrastructure quality assessment in africa using satellite imagery and deep learning"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "tensorflow a system for large scale machine learning"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-4 (induced by source paper "streetify using street view imagery and deep learning for urban streets development"):
  with members:
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "semantic binary segmentation using convolutional networks without decoders"
    - "fully convolutional network for automatic road extraction from satellite imagery"
    - "deep residual learning for image recognition"
    - "rethinking the inception architecture for computer vision"
    - "learning deep features for discriminative localization"

Connected through hyperedge HE-5 (induced by source paper "efficient machine learning for large scale urban land use forecasting in sub saharan africa"):
  with members:
    - "deep convolutional neural network for plant seedlings classification"
    - "remote sensing image scene classification benchmark and state of the art"
    - "deep residual learning for image recognition"
    - "deepsat a learning framework for satellite imagery"

Connected through hyperedge HE-6 (induced by source paper "spatial sensitivity analysis for urban land use prediction with physics constrained conditional generative adversarial networks"):
  with members:
    - "improved techniques for training gans"

Connected through hyperedge HE-7 (induced by source paper "travel time estimation without road networks an urban morphological layout representation approach"):
  with members:
    - "deeptravel a neural network based travel time estimation model with auxiliary supervision"

Connected through hyperedge HE-8 (induced by source paper "xview objects in context in overhead imagery"):
  with members:
    - "zoom out and in network with map attention decision for region proposal and object detection"
    - "a large contextual dataset for classification detection and counting of cars with deep learning"
    - "a unified multi scale deep convolutional neural network for fast object detection"
    - "ssd single shot multibox detector"
    - "matching networks for one shot learning"
    - "low shot visual recognition by shrinking and hallucinating features"
    - "r fcn object detection via region based fully convolutional networks"
    - "faster r cnn towards real time object detection with region proposal networks"

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)