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: "automatic building extraction in aerial scenes using convolutional networks"
  Abstract: Automatic building extraction from aerial and satellite imagery is highly challenging due to extremely large variations of building appearances. To attack this problem, we design a convolutional network with a final stage that integrates activations from multiple preceding stages for pixel-wise prediction, and introduce the signed distance function of building boundaries as the output representation, which has an enhanced representation power. We leverage abundant building footprint data available from geographic information systems (GIS) to compile training data. The trained network achieves superior performance on datasets that are significantly larger and more complex than those used in prior work, demonstrating that the proposed method provides a promising and scalable solution for automating this labor-intensive task.

Connected through hyperedge HE-1 (induced by source paper "infrastructure quality assessment in africa using satellite imagery and deep learning"):
  with members:
    - "using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale"
    - "using deep learning and satellite imagery to quantify the impact of the built environment on neighborhood crime rates"
    - "beyond rgb very high resolution urban remote sensing with multimodal deep networks"
    - "unsupervised deep feature extraction for remote sensing image classification"
    - "r fcn object detection via region based fully convolutional networks"
    - "deep residual learning for image recognition"
    - "land use classification in remote sensing images by convolutional neural networks"
    - "transfer learning from deep features for remote sensing and poverty mapping"

Connected through hyperedge HE-2 (induced by source paper "effective use of dilated convolutions for segmenting small object instances in remote sensing imagery"):
  with members:
    - "dilated residual networks"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "understanding convolution for semantic segmentation"
    - "high resolution aerial image labeling with convolutional neural networks"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "bottom up instance segmentation using deep higher order crfs"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"
    - "learning to refine object segments"

Connected through hyperedge HE-3 (induced by source paper "multi task learning for segmentation of building footprints with deep neural networks"):
  with members:
    - "multi task learning using uncertainty to weigh losses for scene geometry and semantics"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "high resolution aerial image labeling with convolutional neural networks"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "rethinking atrous convolution for semantic image segmentation"
    - "eurosat a novel dataset and deep learning benchmark for land use and land cover classification"
    - "boundary aware instance segmentation"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"

Connected through hyperedge HE-4 (induced by source paper "joint learning from earth observation and openstreetmap data to get faster better semantic maps"):
  with members:
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "image to image translation with conditional adversarial networks"
    - "semantic segmentation of earth observation data using multimodal and multi scale deep networks"
    - "classification with an edge improving semantic image segmentation with boundary detection"
    - "aerial image geolocalization from recognition and matching of roads and intersections"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-5 (induced by source paper "a multi task convolutional neural network for mega city analysis using very high resolution satellite imagery and geospatial data"):
  with members:
    - "faster r cnn towards real time object detection with region proposal networks"
    - "mxnet a flexible and efficient machine learning library for heterogeneous distributed systems"
    - "depth map prediction from a single image using a multi scale deep network"

Connected through hyperedge HE-6 (induced by source paper "building detection from satellite images on a global scale"):
  with members:
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"
    - "fully convolutional networks for semantic segmentation"

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)