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: "algorithms for semantic segmentation of multispectral remote sensing imagery using deep learning"
  Abstract: Abstract   Deep convolutional neural networks (DCNNs) have been used to achieve state-of-the-art performance on many computer vision tasks (e.g., object recognition, object detection, semantic segmentation) thanks to a large repository of annotated image data. Large labeled datasets for other sensor modalities, e.g., multispectral imagery (MSI), are not available due to the large cost and manpower required. In this paper, we adapt state-of-the-art DCNN frameworks in computer vision for semantic segmentation for MSI imagery. To overcome label scarcity for MSI data, we substitute real MSI for generated synthetic MSI in order to initialize a DCNN framework. We evaluate our network initialization scheme on the new RIT-18 dataset that we present in this paper. This dataset contains very-high resolution MSI collected by an unmanned aircraft system. The models initialized with synthetic imagery were less prone to over-fitting and provide a state-of-the-art baseline for future work.

Connected through hyperedge HE-1 (induced by source paper "elkppnet an edge aware neural network with large kernel pyramid pooling for learning discriminative features in semantic segmentation"):
  with members:
    - "dfanet deep feature aggregation for real time semantic segmentation"
    - "learning a discriminative feature network for semantic segmentation"
    - "semantic edge detection with diverse deep supervision"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "focal loss for dense object detection"
    - "incorporating depth into both cnn and crf for indoor semantic segmentation"
    - "richer convolutional features for edge detection"
    - "on support relations and semantic scene graphs"

Connected through hyperedge HE-2 (induced by source paper "map net multi attending path neural network for building footprint extraction from remote sensed imagery"):
  with members:
    - "esfnet efficient network for building extraction from high resolution aerial images"
    - "gcnet non local networks meet squeeze excitation networks and beyond"
    - "high resolution representations for labeling pixels and regions"
    - "deep high resolution representation learning for human pose estimation"
    - "ocnet object context network for scene parsing"
    - "automatic pixelwise object labeling for aerial imagery using stacked u nets"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "dual attention network for scene segmentation"

Connected through hyperedge HE-3 (induced by source paper "active learning for uav based semantic mapping"):
  with members:
    - "detecting the unexpected via image resynthesis"
    - "sampling based incremental information gathering with applications to robotic exploration and environmental monitoring"
    - "the fishyscapes benchmark measuring blind spots in semantic segmentation"
    - "modular sensor fusion for semantic segmentation"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "generative probabilistic novelty detection with adversarial autoencoders"
    - "cost effective active learning for deep image classification"
    - "distance based confidence score for neural network classifiers"

Connected through hyperedge HE-4 (induced by source paper "temporary removal aerial imagery for roof segmentation a large scale dataset towards automatic mapping of buildings"):
  with members:
    - "land cover mapping at very high resolution with rotation equivariant cnns towards small yet accurate models"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "feature pyramid networks for object detection"
    - "pyramid scene parsing network"
    - "attention to scale scale aware semantic image segmentation"
    - "deep residual learning for image recognition"
    - "learning deconvolution network for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "encoder decoder based cnn and fully connected crfs for remote sensed image segmentation"):
  with members:
    - "large receptive field networks for high scale image super resolution"
    - "understanding the effective receptive field in deep convolutional neural networks"
    - "learning to refine object segments"
    - "multi scale context aggregation by dilated convolutions"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "fully convolutional networks for semantic segmentation"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

Connected through hyperedge HE-6 (induced by source paper "weedmap a large scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming"):
  with members:
    - "fully convolutional networks with sequential information for robust crop and weed detection in precision farming"
    - "deep learning in agriculture a survey"
    - "automatic model based dataset generation for fast and accurate crop and weeds detection"
    - "h denseunet hybrid densely connected unet for liver and liver tumor segmentation from ct volumes"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "enet a deep neural network architecture for real time semantic segmentation"
    - "weednet dense semantic weed classification using multispectral images and mav for smart farming"
    - "a review on deep learning techniques applied to semantic segmentation"

Connected through hyperedge HE-7 (induced by source paper "an informative path planning framework for uav based terrain monitoring"):
  with members:
    - "sampling based incremental information gathering with applications to robotic exploration and environmental monitoring"
    - "hindsight is only 50 50 unsuitability of mdp based approximate pomdp solvers for multi resolution information gathering"
    - "a data driven approach for autonomous motion planning and control in off road driving scenarios"
    - "multiresolution mapping and informative path planning for uav based terrain monitoring"
    - "dynamic system identification and control for a cost effective open source vtol mav"
    - "weednet dense semantic weed classification using multispectral images and mav for smart farming"
    - "modeling curiosity in a mobile robot for long term autonomous exploration and monitoring"
    - "pomdp lite for robust robot planning under uncertainty"

Connected through hyperedge HE-8 (induced by source paper "treesegnet adaptive tree cnns for subdecimeter aerial image segmentation"):
  with members:
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "deepunet a deep fully convolutional network for pixel level sea land segmentation"
    - "dense semantic labeling of subdecimeter resolution images with convolutional neural networks"
    - "multi task learning for segmentation of building footprints with deep neural networks"
    - "distilling a neural network into a soft decision tree"
    - "rethinking atrous convolution for semantic image segmentation"
    - "fully convolutional networks for dense semantic labelling of high resolution aerial imagery"
    - "conditional computation in neural networks for faster models"

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)