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: "a multi scale analysis of 27 000 urban street networks every us city town urbanized area and zillow neighborhood"
  Abstract: OpenStreetMap offers a valuable source of worldwide geospatial data useful to urban researchers. This study uses the OSMnx software to automatically download and analyze 27,000 US street networks from OpenStreetMap at metropolitan, municipal, and neighborhood scales - namely, every US city and town, census urbanized area, and Zillow-defined neighborhood. It presents empirical findings on US urban form and street network characteristics, emphasizing measures relevant to graph theory, transportation, urban design, and morphology such as structure, connectedness, density, centrality, and resilience. In the past, street network data acquisition and processing have been challenging and ad hoc. This study illustrates the use of OSMnx and OpenStreetMap to consistently conduct street network analysis with extremely large sample sizes, with clearly defined network definitions and extents for reproducibility, and using nonplanar, directed graphs. These street networks and measures data have been shared in a public repository for other researchers to use.

Connected through hyperedge HE-1 (induced by source paper "role detection in bicycle sharing networks using multilayer stochastic block models"):
  with members:
    - "relating modularity maximization and stochastic block models in multilayer networks"
    - "community discovery in dynamic networks a survey"
    - "random graph models for dynamic networks"
    - "the ground truth about metadata and community detection in networks"
    - "a framework for the construction of generative models for mesoscale structure in multilayer networks"
    - "clustering network layers with the strata multilayer stochastic block model"
    - "equivalence between modularity optimization and maximum likelihood methods for community detection"
    - "generative benchmark models for mesoscale structures in multilayer networks"

Connected through hyperedge HE-2 (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"
    - "urban morphology meets deep learning exploring urban forms in one million cities town and villages across the planet"
    - "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-3 (induced by source paper "modelling urban networks using variational autoencoders"):
  with members:
    - "neural turtle graphics for modeling city road layouts"
    - "modeling urbanization patterns with generative adversarial networks"
    - "a unified approach to interpreting model predictions"
    - "learning important features through propagating activation differences"
    - "urban morphology meets deep learning exploring urban forms in one million cities town and villages across the planet"
    - "why should i trust you explaining the predictions of any classifier"
    - "on the quantitative analysis of decoder based generative models"

Connected through hyperedge HE-4 (induced by source paper "learning from discovering 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"
    - "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"
    - "very deep convolutional networks for large scale 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.SI(Social and Information Networks)