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: "functional map of the world"
  Abstract: We present a new dataset, Functional Map of the World (fMoW), which aims to inspire the development of machine learning models capable of predicting the functional purpose of buildings and land use from temporal sequences of satellite images and a rich set of metadata features. The metadata provided with each image enables reasoning about location, time, sun angles, physical sizes, and other features when making predictions about objects in the image. Our dataset consists of over 1 million images from over 200 countries. For each image, we provide at least one bounding box annotation containing one of 63 categories, including a "false detection" category. We present an analysis of the dataset along with baseline approaches that reason about metadata and temporal views. Our data, code, and pretrained models have been made publicly available.

Connected through hyperedge HE-1 (induced by source paper "learning when and where to zoom with deep reinforcement learning"):
  with members:
    - "predicting economic development using geolocated wikipedia articles"
    - "approximating cnns with bag of local features models works surprisingly well on imagenet"
    - "on low resolution face recognition in the wild comparisons and new techniques"
    - "learning to interpret satellite images in global scale using wikipedia"
    - "efficient object detection in large images using deep reinforcement learning"
    - "learning to interpret satellite images using wikipedia"
    - "dropblock a regularization method for convolutional networks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"

Connected through hyperedge HE-2 (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"
    - "deepglobe 2018 a challenge to parse the earth through satellite images"
    - "where to put the image in an image caption generator"
    - "remote sensing image scene classification benchmark and state of the art"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-3 (induced by source paper "spacenet mvoi a multi view overhead imagery dataset"):
  with members:
    - "spacenet a remote sensing dataset and challenge series"
    - "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"

Connected through hyperedge HE-4 (induced by source paper "learning to interpret satellite images in global scale using wikipedia"):
  with members:
    - "predicting economic development using geolocated wikipedia articles"
    - "learning to interpret satellite images using wikipedia"
    - "adversarial constraint learning for structured prediction"
    - "tile2vec unsupervised representation learning for spatially distributed data"
    - "exploring the limits of weakly supervised pretraining"
    - "learning two branch neural networks for image text matching tasks"
    - "snorkel rapid training data creation with weak supervision"
    - "aerial vehicle tracking by adaptive fusion of hyperspectral likelihood maps"

Connected through hyperedge HE-5 (induced by source paper "hydra an ensemble of convolutional neural networks for geospatial land classification"):
  with members:
    - "trunk branch ensemble convolutional neural networks for video based face recognition"
    - "aid a benchmark data set for performance evaluation of aerial scene classification"
    - "the effectiveness of data augmentation in image classification using deep learning"
    - "remote sensing image scene classification benchmark and state of the art"
    - "towards better exploiting convolutional neural networks for remote sensing scene classification"
    - "xception deep learning with depthwise separable convolutions"
    - "understanding data augmentation for classification when to warp"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-6 (induced by source paper "adversarial examples in remote sensing"):
  with members:
    - "adversarial patch"
    - "adversarial spheres"
    - "shapeshifter robust physical adversarial attack on faster r cnn object detector"
    - "on the robustness of the cvpr 2018 white box adversarial example defenses"
    - "robust physical world attacks on deep learning models"
    - "no need to worry about adversarial examples in object detection in autonomous vehicles"
    - "adversarial examples are not easily detected bypassing ten detection methods"
    - "synthesizing robust adversarial examples"

Connected through hyperedge HE-7 (induced by source paper "learning to interpret satellite images using wikipedia"):
  with members:
    - "adversarial constraint learning for structured prediction"
    - "tile2vec unsupervised representation learning for spatially distributed data"
    - "exploring the limits of weakly supervised pretraining"
    - "joint learning from earth observation and openstreetmap data to get faster better semantic maps"
    - "snorkel rapid training data creation with weak supervision"
    - "yolo9000 better faster stronger"
    - "label free supervision of neural networks with physics and domain knowledge"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-8 (induced by source paper "spacenet a remote sensing dataset and challenge series"):
  with members:
    - "xview objects in context in overhead imagery"
    - "you only look twice rapid multi scale object detection in satellite imagery"
    - "a multi task convolutional neural network for mega city analysis using very high resolution satellite imagery and geospatial data"
    - "osmnx new methods for acquiring constructing analyzing and visualizing complex street networks"
    - "a large contextual dataset for classification detection and counting of cars with deep learning"
    - "yolo9000 better faster stronger"
    - "torontocity seeing the world with a million eyes"
    - "pyramid scene parsing network"

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)