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: "feature pyramid network for multi class land segmentation"
  Abstract: Semantic segmentation is in-demand in satellite imagery processing. Because of the complex environment, automatic categorization and segmentation of land cover is a challenging problem. Solving it can help to overcome many obstacles in urban planning, environmental engineering or natural landscape monitoring. In this paper, we propose an approach for automatic multi-class land segmentation based on a fully convolutional neural network of feature pyramid network (FPN) family. This network is consisted of pre-trained on ImageNet Resnet50 encoder and neatly developed decoder. Based on validation results, leaderboard score and our own experience this network shows reliable results for the DEEPGLOBE - CVPR 2018 land cover classification sub-challenge. Moreover, this network moderately uses memory that allows using GTX 1080 or 1080 TI video cards to perform whole training and makes pretty fast predictions.

Connected through hyperedge HE-1 (induced by source paper "dense dilated convolutions merging network for land cover classification"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "dense fusion classmate network for land cover classification"
    - "on the convergence of adam and beyond"
    - "dense dilated convolutions merging network for semantic mapping of remote sensing images"
    - "sdc stacked dilated convolution a unified descriptor network for dense matching tasks"
    - "uncertainty gated network for land cover segmentation"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "urban land cover classification with missing data modalities using deep convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "albumentations fast and flexible image augmentations"):
  with members:
    - "camera model identification using convolutional neural networks"
    - "paediatric bone age assessment using deep convolutional neural networks"
    - "angiodysplasia detection and localization using deep convolutional neural networks"
    - "fully convolutional network for automatic road extraction from satellite imagery"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "autoaugment learning augmentation policies from data"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-3 (induced by source paper "hran hybrid residual attention network for single image super resolution"):
  with members:
    - "lightweight image super resolution with adaptive weighted learning network"
    - "distilling with residual network for single image super resolution"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "image super resolution using very deep residual channel attention networks"
    - "residual dense network for image super resolution"
    - "deep back projection networks for super resolution"
    - "big little net an efficient multi scale feature representation for visual and speech recognition"
    - "deep laplacian pyramid networks for fast and accurate super resolution"

Connected through hyperedge HE-4 (induced by source paper "geometric graph convolutional neural networks"):
  with members:
    - "simplifying graph convolutional networks"
    - "enhanced deep learning prediction of molecular properties via augmentation of bond topology"
    - "adaptive graph convolutional neural networks"
    - "how powerful are graph neural networks"
    - "splinecnn fast geometric deep learning with continuous b spline kernels"
    - "neural message passing for quantum chemistry"
    - "graph convolution a high order and adaptive approach"
    - "gated graph sequence neural networks"

Connected through hyperedge HE-5 (induced by source paper "multi scale embedded cnn for music tagging mse cnn"):
  with members:
    - "panoptic feature pyramid networks"
    - "sample level deep convolutional neural networks for music auto tagging using raw waveforms"
    - "end to end learning for music audio tagging at scale"
    - "timbre analysis of music audio signals with convolutional neural networks"
    - "feature pyramid networks for object detection"
    - "automatic tagging using deep convolutional neural networks"
    - "learning multiscale features directly from waveforms"
    - "convolutional recurrent neural networks for music classification"

Connected through hyperedge HE-6 (induced by source paper "camera model identification using convolutional neural networks"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "deep convolutional neural networks for breast cancer histology image analysis"
    - "paediatric bone age assessment using deep convolutional neural networks"
    - "angiodysplasia detection and localization using deep convolutional neural networks"
    - "fully convolutional network for automatic road extraction from satellite imagery"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "aligned and non aligned double jpeg detection using convolutional neural 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)