human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "longevity associated geometry identified in satellite images sidewalks driveways and hiking trails"):
  Abstract: Importance: Following a century of increase, life expectancy in the United States has stagnated and begun to decline in recent decades. Using satellite images and street view images prior work has demonstrated associations of the built environment with income, education, access to care and health factors such as obesity. However, assessment of learned image feature relationships with variation in crude mortality rate across the United States has been lacking. #R##N#Objective: Investigate prediction of county-level mortality rates in the U.S. using satellite images. #R##N#Design: Satellite images were extracted with the Google Static Maps application programming interface for 430 counties representing approximately 68.9% of the US population. A convolutional neural network was trained using crude mortality rates for each county in 2015 to predict mortality. Learned image features were interpreted using Shapley Additive Feature Explanations, clustered, and compared to mortality and its associated covariate predictors. #R##N#Main Outcomes and Measures: County mortality was predicted using satellite images. #R##N#Results: Predicted mortality from satellite images in a held-out test set of counties was strongly correlated to the true crude mortality rate (Pearson r=0.72). Learned image features were clustered, and we identified 10 clusters that were associated with education, income, geographical region, race and age. #R##N#Conclusion and Relevance: The application of deep learning techniques to remotely-sensed features of the built environment can serve as a useful predictor of mortality in the United States. Tools that are able to identify image features associated with health-related outcomes can inform targeted public health interventions.

Includes members:
  Member "the effects of super resolution on object detection performance in satellite imagery":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "an end to end framework for low resolution remote sensing semantic segmentation")
  Member "use of deep learning to examine the association of the built environment with prevalence of neighborhood adult obesity":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "deep landscape features for improving vector borne disease prediction")
  Member "tile2vec unsupervised representation learning for spatially distributed data":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "urban2vec incorporating street view imagery and pois for multi modal urban neighborhood embedding")
  Member "geometry consistent generative adversarial networks for one sided unsupervised domain mapping":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deformation aware unpaired image translation for pose estimation on laboratory animals")
  Member "a unified approach to interpreting model predictions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "causal interpretability for machine learning problems methods and evaluation")
  Member "a deep learning approach for population estimation from satellite imagery":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a multidisciplinary survey and framework for design and evaluation of explainable ai systems")
  Member "unpaired image to image translation using cycle consistent adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "generative adversarial networks in computer vision a survey and taxonomy")
  Member "satellite imagery feature detection using deep convolutional neural network a kaggle competition":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "the synthinel 1 dataset a collection of high resolution synthetic overhead imagery for building segmentation")

Question: Please tell me which class the source paper 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)