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 "learning from discovering an unsupervised approach to geographical knowledge discovery using street level and street network images"):
  Abstract: Recent researches have shown the increasing use of machine learning methods in geography and urban analytics, primarily to extract features and patterns from spatial and temporal data. Research, integrating geographical processes in machine learning models and, leveraging on geographical information to better interpret these methods had been sparse. This research contributes to the ladder, where we show how latent variables learned from unsupervised learning methods can be used for geographic knowledge discovery. In particular, we propose a simple and novel approach called Convolutional-PCA (ConvPCA) which are applied on both street level and street network images in finding a set of uncorrelated visual latent responses. The approach allows for meaningful explanations using a combination of, geographical and generative visualizations to explore the latent space, and to show how the learned embeddings can be used to predict urban characteristics such as street-level enclosures and street network density.

Includes members:
  Member "modelling urban networks using variational autoencoders":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "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 "an unsupervised approach to geographical knowledge discovery using street level and street network images")
  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-3 (induced by source paper "role detection in bicycle sharing networks using multilayer stochastic block models")
  Member "osmnx new methods for acquiring constructing analyzing and visualizing complex street networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an unsupervised approach to geographical knowledge discovery using street level and street network images")
  Member "deep learning the city quantifying urban perception at a global scale":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "facelift a transparent deep learning framework to beautify urban scenes")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discrimination aware network pruning for deep model compression")

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)