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: "deepglobe 2018 a challenge to parse the earth through satellite images"
  Abstract: We present the DeepGlobe 2018 Satellite Image Understanding Challenge, which includes three public competitions for segmentation, detection, and classification tasks on satellite images (Figure 1). Similar to other challenges in computer vision domain such as DAVIS[21] and COCO[33], DeepGlobe proposes three datasets and corresponding evaluation methodologies, coherently bundled in three competitions with a dedicated workshop co-located with CVPR 2018. We observed that satellite imagery is a rich and structured source of information, yet it is less investigated than everyday images by computer vision researchers. However, bridging modern computer vision with remote sensing data analysis could have critical impact to the way we understand our environment and lead to major breakthroughs in global urban planning or climate change research. Keeping such bridging objective in mind, DeepGlobe aims to bring together researchers from different domains to raise awareness of remote sensing in the computer vision community and vice-versa. We aim to improve and evaluate state-of-the-art satellite image understanding approaches, which can hopefully serve as reference benchmarks for future research in the same topic. In this paper, we analyze characteristics of each dataset, define the evaluation criteria of the competitions, and provide baselines for each task.

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"
    - "urban land cover classification with missing data modalities using deep convolutional neural networks"
    - "rifcn recurrent network in fully convolutional network for semantic segmentation of high resolution remote sensing images"

Connected through hyperedge HE-2 (induced by source paper "agriculture vision a large aerial image database for agricultural pattern analysis"):
  with members:
    - "foal fast online adaptive learning for cardiac motion estimation"
    - "higherhrnet scale aware representation learning for bottom up human pose estimation"
    - "differential treatment for stuff and things a simple unsupervised domain adaptation method for semantic segmentation"
    - "convolutional networks with dense connectivity"
    - "bottom up higher resolution networks for multi person pose estimation"
    - "isaid a large scale dataset for instance segmentation in aerial images"
    - "any precision deep neural networks"
    - "skynet a hardware efficient method for object detection and tracking on embedded systems"

Connected through hyperedge HE-3 (induced by source paper "rsvqa visual question answering for remote sensing data"):
  with members:
    - "dense fusion classmate network for land cover classification"
    - "correcting rural building annotations in openstreetmap using convolutional neural networks"
    - "r 3 net a deep network for multioriented vehicle detection in aerial images and videos"
    - "answer them all toward universal visual question answering models"
    - "learning to count objects in natural images for visual question answering"
    - "vizwiz grand challenge answering visual questions from blind people"
    - "mutan multimodal tucker fusion for visual question answering"
    - "dota a large scale dataset for object detection in aerial images"

Connected through hyperedge HE-4 (induced by source paper "the synthinel 1 dataset a collection of high resolution synthetic overhead imagery for building segmentation"):
  with members:
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "training deep networks with synthetic data bridging the reality gap by domain randomization"
    - "domain randomization for transferring deep neural networks from simulation to the real world"
    - "dota a large scale dataset for object detection in aerial images"
    - "learning from synthetic data addressing domain shift for semantic segmentation"
    - "satellite imagery feature detection using deep convolutional neural network a kaggle competition"
    - "rethinking atrous convolution for semantic image segmentation"

Connected through hyperedge HE-5 (induced by source paper "discoverability in satellite imagery a good sentence is worth a thousand pictures"):
  with members:
    - "eda easy data augmentation techniques for boosting performance on text classification tasks"
    - "hello it s gpt 2 how can i help you towards the use of pretrained language models for task oriented dialogue systems"
    - "xview objects in context in overhead imagery"
    - "exploring models and data for remote sensing image caption generation"
    - "where to put the image in an image caption generator"
    - "functional map of the world"
    - "remote sensing image scene classification benchmark and state of the art"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-6 (induced by source paper "collaborative global local networks for memory efficient segmentation of ultra high resolution images"):
  with members:
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "guided upsampling network for real time semantic segmentation"
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "contextnet exploring context and detail for semantic segmentation in real time"
    - "connecting image denoising and high level vision tasks via deep learning"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "focal loss for dense object detection"
    - "when image denoising meets high level vision tasks a deep learning approach"

Connected through hyperedge HE-7 (induced by source paper "a comprehensive analysis of weakly supervised semantic segmentation in different image domains"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "dense fusion classmate network for land cover classification"
    - "weakly supervised learning of instance segmentation with inter pixel relations"
    - "the apolloscape open dataset for autonomous driving and its application"
    - "constrained cnn losses for weakly supervised segmentation"
    - "weakly supervised semantic segmentation of satellite images"
    - "weakly supervised instance segmentation using class peak response"
    - "learning pixel level semantic affinity with image level supervision for weakly supervised semantic segmentation"

Connected through hyperedge HE-8 (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"
    - "switching convolutional neural network for crowd counting"
    - "decidenet counting varying density crowds through attention guided detection and density estimation"
    - "cross view image matching for geo localization in urban environments"

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)