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: "temporary removal aerial imagery for roof segmentation a large scale dataset towards automatic mapping of buildings"
  Abstract: As an important branch of deep learning, convolutional neural network has largely improved the performance of building detection. For further accelerating the development of building detection toward automatic mapping, a benchmark dataset bears significance in fair comparisons. However, several problems still remain in the current public datasets that address this task. First, although building detection is generally considered equivalent to extracting roof outlines, most datasets directly provide building footprints as ground truths for testing and evaluation; the challenges of these benchmarks are more complicated than roof segmentation, as relief displacement leads to varying degrees of misalignment between roof outlines and footprints. On the other hand, an image dataset should feature a large quantity and high spatial resolution to effectively train a high-performance deep learning model for accurate mapping of buildings. Unfortunately, the remote sensing community still lacks proper benchmark datasets that can simultaneously satisfy these requirements. In this paper, we present a new large-scale benchmark dataset termed Aerial Imagery for Roof Segmentation (AIRS). This dataset provides a wide coverage of aerial imagery with 7.5 cm resolution and contains over 220,000 buildings. The task posed for AIRS is defined as roof segmentation. We implement several state-of-the-art deep learning methods of semantic segmentation for performance evaluation and analysis of the proposed dataset. The results can serve as the baseline for future work.

Connected through hyperedge HE-1 (induced by source paper "semantic segmentation from remote sensor data and the exploitation of latent learning for classification of auxiliary tasks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "self supervised generalisation with meta auxiliary learning"
    - "fine grained visual categorization using meta learning optimization with sample selection of auxiliary data"
    - "automatic pixelwise object labeling for aerial imagery using stacked u nets"
    - "net2vec quantifying and explaining how concepts are encoded by filters in deep neural networks"
    - "auxiliary tasks in multi task learning"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "concurrent spatial and channel squeeze excitation in fully convolutional 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:
    - "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"
    - "deepsd generating high resolution climate change projections through single image super resolution"
    - "adversarial deep structural networks for mammographic mass segmentation"
    - "semantic segmentation using adversarial networks"

Connected through hyperedge HE-3 (induced by source paper "building segmentation through a gated graph convolutional neural network with deep structured feature embedding"):
  with members:
    - "building footprint generation using improved generative adversarial networks"
    - "a feature embedding strategy for high level cnn representations from multiple convnets"
    - "gated graph sequence neural networks"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "semi supervised classification with graph convolutional networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "torontocity seeing the world with a million eyes"

Connected through hyperedge HE-4 (induced by source paper "robust object extraction from remote sensing data"):
  with members:
    - "deep multi task learning for a geographically regularized semantic segmentation of aerial images"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "multiscale combinatorial grouping for image segmentation and object proposal generation"
    - "a survey on object detection in optical remote sensing images"

Connected through hyperedge HE-5 (induced by source paper "geoseg a computer vision package for automatic building segmentation and outline extraction"):
  with members:
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "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)