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: "spacenet a remote sensing dataset and challenge series"
  Abstract: Foundational mapping remains a challenge in many parts of the world, particularly in dynamic scenarios such as natural disasters when timely updates are critical. Updating maps is currently a highly manual process requiring a large number of human labelers to either create features or rigorously validate automated outputs. We propose that the frequent revisits of earth imaging satellite constellations may accelerate existing efforts to quickly update foundational maps when combined with advanced machine learning techniques. Accordingly, the SpaceNet partners (CosmiQ Works, Radiant Solutions, and NVIDIA), released a large corpus of labeled satellite imagery on Amazon Web Services (AWS) called SpaceNet. The SpaceNet partners also launched a series of public prize competitions to encourage improvement of remote sensing machine learning algorithms. The first two of these competitions focused on automated building footprint extraction, and the most recent challenge focused on road network extraction. In this paper we discuss the SpaceNet imagery, labels, evaluation metrics, prize challenge results to date, and future plans for the SpaceNet challenge series.

Connected through hyperedge HE-1 (induced by source paper "road network and travel time extraction from multiple look angles with spacenet data"):
  with members:
    - "city scale road extraction from satellite imagery v2 road speeds and travel times"
    - "spacenet mvoi a multi view overhead imagery dataset"
    - "from satellite imagery to disaster insights"
    - "deep learning in agriculture a survey"
    - "benchmark dataset for automatic damaged building detection from post hurricane remotely sensed imagery"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "a large dataset of object scans"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "deepsat v2 feature augmented convolutional neural nets for satellite image classification"):
  with members:
    - "fusing deep learned and hand crafted features of appearance shape and dynamics for automatic pain estimation"
    - "deepsat a learning framework for satellite imagery"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-3 (induced by source paper "conv mpn convolutional message passing neural network for structured outdoor architecture reconstruction"):
  with members:
    - "floor sp inverse cad for floorplans by sequential room wise shortest path"
    - "ppgnet learning point pair graph for line segment detection"
    - "deep high resolution representation learning for human pose estimation"
    - "end to end wireframe parsing"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "simple baselines for human pose estimation and tracking"
    - "monocular total capture posing face body and hands in the wild"
    - "unsupervised image to sequence translation with canvas drawer networks"

Connected through hyperedge HE-4 (induced by source paper "spacenet mvoi a multi view overhead imagery dataset"):
  with members:
    - "xview objects in context in overhead imagery"
    - "spatially transformed adversarial examples"
    - "an occluded stacked hourglass approach to facial landmark localization and occlusion estimation"
    - "the effects of super resolution on object detection performance in satellite imagery"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "you only look twice rapid multi scale object detection in satellite imagery"
    - "why do deep convolutional networks generalize so poorly to small image transformations"

Connected through hyperedge HE-5 (induced by source paper "image conditioned graph generation for road network extraction"):
  with members:
    - "neural turtle graphics for modeling city road layouts"
    - "path augmented graph transformer network"
    - "roberta a robustly optimized bert pretraining approach"
    - "efficient graph generation with graph recurrent attention networks"
    - "neural particle smoothing for sampling from conditional sequence models"
    - "constrained graph variational autoencoders for molecule design"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "graph r cnn for scene graph generation"

Connected through hyperedge HE-6 (induced by source paper "revisiting classical bagging with modern transfer learning for on the fly disaster damage detector"):
  with members:
    - "when does label smoothing help"
    - "a closer look at few shot classification"
    - "using pre training can improve model robustness and uncertainty"
    - "rethinking imagenet pre training"
    - "rare event detection using disentangled representation learning"
    - "from satellite imagery to disaster insights"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "deep anomaly detection with outlier exposure"

Connected through hyperedge HE-7 (induced by source paper "city scale road extraction from satellite imagery v2 road speeds and travel times"):
  with members:
    - "city scale road extraction from satellite imagery"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "road extraction by deep residual u net"
    - "roadtracer automatic extraction of road networks from aerial images"
    - "focal loss for dense object detection"
    - "beyond the pixel wise loss for topology aware delineation"
    - "torontocity seeing the world with a million eyes"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-8 (induced by source paper "towards reliable evaluation of road network reconstructions"):
  with members:
    - "neural turtle graphics for modeling city road layouts"
    - "joint segmentation and path classification of curvilinear structures"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "csrnet dilated convolutional neural networks for understanding the highly congested scenes"
    - "roadtracer automatic extraction of road networks from aerial images"
    - "linknet exploiting encoder representations for efficient semantic segmentation"
    - "beyond the pixel wise loss for topology aware delineation"
    - "u net convolutional networks for biomedical image 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)