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: "urban morphology meets deep learning exploring urban forms in one million cities town and villages across the planet"
  Abstract: Study of urban form is an important area of research in urban planning/design that contributes to our understanding of how cities function and evolve. However, classical approaches are based on very limited observations and inconsistent methods. As an alternative, availability of massive urban data collections such as Open Street Map from the one hand and the recent advancements in machine learning methods such as deep learning techniques on the other have opened up new possibilities to automatically investigate urban forms at the global scale. In this work for the first time, by collecting a large data set of street networks in more than one million cities, towns and villages all over the world, we trained a deep convolutional auto-encoder, that automatically learns the hierarchical structures of urban forms and represents them via dense and comparable vectors. We showed how the learned urban vectors could be used for different investigations. Using the learned urban vectors, one is able to easily find and compare similar urban forms all over the world, considering their overall spatial structure and other factors such as orientation, graphical structure, and density and partial deformations. Further cluster analysis reveals the distribution of the main patterns of urban forms all over the planet.

Connected through hyperedge HE-1 (induced by source paper "an unsupervised approach to geographical knowledge discovery using street level and street network images"):
  with members:
    - "modelling urban networks using variational autoencoders"
    - "augmenting correlation structures in spatial data using deep generative models"
    - "a pca like autoencoder"
    - "video to video synthesis"
    - "a multi scale analysis of 27 000 urban street networks every us city town urbanized area and zillow neighborhood"
    - "osmnx new methods for acquiring constructing analyzing and visualizing complex street networks"
    - "deep learning the city quantifying urban perception at a global scale"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-2 (induced by source paper "modelling urban networks using variational autoencoders"):
  with members:
    - "neural turtle graphics for modeling city road layouts"
    - "a multi scale analysis of 27 000 urban street networks every us city town urbanized area and zillow neighborhood"
    - "modeling urbanization patterns with generative adversarial networks"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"
    - "why should i trust you explaining the predictions of any classifier"
    - "on the quantitative analysis of decoder based generative models"

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)