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: "tile2vec unsupervised representation learning for spatially distributed data"
  Abstract: Geospatial analysis lacks methods like the word vector representations and pre-trained networks that significantly boost performance across a wide range of natural language and computer vision tasks. To fill this gap, we introduce Tile2Vec, an unsupervised representation learning algorithm that extends the distributional hypothesis from natural language -- words appearing in similar contexts tend to have similar meanings -- to spatially distributed data. We demonstrate empirically that Tile2Vec learns semantically meaningful representations on three datasets. Our learned representations significantly improve performance in downstream classification tasks and, similar to word vectors, visual analogies can be obtained via simple arithmetic in the latent space.

Connected through hyperedge HE-1 (induced by source paper "urban2vec incorporating street view imagery and pois for multi modal urban neighborhood embedding"):
  with members:
    - "learning from discovering an unsupervised approach to geographical knowledge discovery using street level and street network images"
    - "predicting twitter user socioeconomic attributes with network and language information"
    - "node2vec scalable feature learning for networks"
    - "learning deep structure preserving image text embeddings"
    - "deep multimodal embedding manipulating novel objects with point clouds language and trajectories"
    - "rethinking the inception architecture for computer vision"
    - "deepwalk online learning of social representations"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-2 (induced by source paper "longevity associated geometry identified in satellite images sidewalks driveways and hiking trails"):
  with members:
    - "the effects of super resolution on object detection performance in satellite imagery"
    - "use of deep learning to examine the association of the built environment with prevalence of neighborhood adult obesity"
    - "geometry consistent generative adversarial networks for one sided unsupervised domain mapping"
    - "a unified approach to interpreting model predictions"
    - "a deep learning approach for population estimation from satellite imagery"
    - "unpaired image to image translation using cycle consistent adversarial networks"
    - "satellite imagery feature detection using deep convolutional neural network a kaggle competition"
    - "multi task learning for segmentation of building footprints with deep neural networks"

Connected through hyperedge HE-3 (induced by source paper "augmenting correlation structures in spatial data using deep generative models"):
  with members:
    - "generative adversarial networks for financial trading strategies fine tuning and combination"
    - "coco gan generation by parts via conditional coordinating"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "synthetic data augmentation using gan for improved liver lesion classification"
    - "evolutionary generative adversarial networks"
    - "gpytorch blackbox matrix matrix gaussian process inference with gpu acceleration"
    - "point cloud gan"
    - "freehand ultrasound image simulation with spatially conditioned generative adversarial networks"

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"
    - "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"
    - "learning aerial image segmentation from online maps"

Connected through hyperedge HE-5 (induced by source paper "geostyle discovering fashion trends and events"):
  with members:
    - "exploring the limits of weakly supervised pretraining"
    - "recognition in terra incognita"
    - "streetstyle exploring world wide clothing styles from millions of photos"
    - "learning the latent look unsupervised discovery of a style coherent embedding from fashion images"
    - "fashion forward forecasting visual style in fashion"
    - "mask r cnn"
    - "yfcc100m the new data in multimedia research"
    - "a century of portraits a visual historical record of american high school yearbooks"

Connected through hyperedge HE-6 (induced by source paper "breizhcrops a satellite time series dataset for crop type identification"):
  with members:
    - "attention is all you need"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "learning to interpret satellite images using wikipedia"):
  with members:
    - "adversarial constraint learning for structured prediction"
    - "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"
    - "functional map of the world"
    - "yolo9000 better faster stronger"
    - "label free supervision of neural networks with physics and domain knowledge"
    - "deep residual learning for image recognition"

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)