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 "an unsupervised approach to geographical knowledge discovery using street level and street network images"):
  Abstract: Recent researches have shown the increasing use of machine learn-ing methods in geography and urban analytics, primarily to extract features and patterns from spatial and temporal data using a supervised approach. Researches integrating geographical processes in machine learning models and the use of unsupervised approacheson geographical data for knowledge discovery had been sparse. This research contributes to the ladder, where we show how latent variables learned from unsupervised learning methods on urbanimages can be used for geographic knowledge discovery. In particular, we propose a simple approach called Convolutional-PCA(ConvPCA) which are applied on both street level and street network images to find a set of uncorrelated and ordered visual latentcomponents. The approach allows for meaningful explanations using a combination of geographical and generative visualisations to explore the latent space, and to show how the learned representation can be used to predict urban characteristics such as streetquality and street network attributes. The research also finds that the visual components from the ConvPCA model achieves similaraccuracy when compared to less interpretable dimension reduction techniques.

Includes members:
  Member "modelling urban networks using variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning from discovering an unsupervised approach to geographical knowledge discovery using street level and street network images")
  Member "augmenting correlation structures in spatial data using deep generative models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning from discovering an unsupervised approach to geographical knowledge discovery using street level and street network images")
  Member "a pca like autoencoder":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "deep reasoning networks thinking fast and slow")
  Member "video to video synthesis":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "gan compression efficient architectures for interactive conditional gans")
  Member "a multi scale analysis of 27 000 urban street networks every us city town urbanized area and zillow neighborhood":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "role detection in bicycle sharing networks using multilayer stochastic block models")
  Member "urban morphology meets deep learning exploring urban forms in one million cities town and villages across the planet":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "modelling urban networks using variational autoencoders")
  Member "osmnx new methods for acquiring constructing analyzing and visualizing complex street networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "learning to prune speeding up repeated computations")
  Member "deep learning the city quantifying urban perception at a global scale":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "facelift a transparent deep learning framework to beautify urban scenes")

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)