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: "osmnx new methods for acquiring constructing analyzing and visualizing complex street networks"
  Abstract: Urban scholars have studied street networks in various ways, but there are data availability and consistency limitations to the current urban planning/street network analysis literature. To address these challenges, this article presents OSMnx, a new tool to make the collection of data and creation and analysis of street networks simple, consistent, automatable and sound from the perspectives of graph theory, transportation, and urban design. OSMnx contributes five significant capabilities for researchers and practitioners: first, the automated downloading of political boundaries and building footprints; second, the tailored and automated downloading and constructing of street network data from OpenStreetMap; third, the algorithmic correction of network topology; fourth, the ability to save street networks to disk as shapefiles, GraphML, or SVG files; and fifth, the ability to analyze street networks, including calculating routes, projecting and visualizing networks, and calculating metric and topological measures. These measures include those common in urban design and transportation studies, as well as advanced measures of the structure and topology of the network. Finally, this article presents a simple case study using OSMnx to construct and analyze street networks in Portland, Oregon.

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"
    - "urban morphology meets deep learning exploring urban forms in one million cities town and villages across the planet"
    - "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 "learning to prune speeding up repeated computations"):
  with members:
    - "learning to branch"
    - "leapsandbounds a method for approximately optimal algorithm configuration"
    - "dispersion for data driven algorithm design online learning and private optimization"
    - "online optimization of smoothed piecewise constant functions"
    - "learning theoretic foundations of algorithm configuration for combinatorial partitioning problems"
    - "a pac approach to application specific algorithm selection"
    - "self improving algorithms"

Connected through hyperedge HE-3 (induced by source paper "a probabilistic approach for demand aware ride sharing optimization"):
  with members:
    - "deeppool distributed model free algorithm for ride sharing using deep reinforcement learning"
    - "movi a model free approach to dynamic fleet management"
    - "predictive positioning and quality of service ridesharing for campus mobility on demand systems"
    - "adaptive caching networks with optimality guarantees"
    - "an optimization framework for online ride sharing markets"
    - "optimal geographic caching in cellular networks"

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"
    - "a multi scale analysis of 27 000 urban street networks every us city town urbanized area and zillow neighborhood"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "on the interaction between autonomous mobility on demand systems and power distribution networks an optimal power flow approach"):
  with members:
    - "on the interaction between autonomous mobility on demand systems and the power network models and coordination algorithms"
    - "optimal pricing to manage electric vehicles in coupled power and transportation networks"
    - "tools for improving resilience of electric distribution systems with networked microgrids"
    - "data driven model predictive control of autonomous mobility on demand systems"
    - "a bcmp network approach to modeling and controlling autonomous mobility on demand systems"
    - "model predictive control of autonomous mobility on demand systems"

Connected through hyperedge HE-6 (induced by source paper "dynamic demand prediction for expanding electric vehicle sharing systems a graph sequence learning approach"):
  with members:
    - "bike flow prediction with multi graph convolutional networks"
    - "deep multi view spatial temporal network for taxi demand prediction"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "semi supervised classification with graph convolutional networks"
    - "spectral networks and locally connected networks on graphs"

Connected through hyperedge HE-7 (induced by source paper "neural approximate dynamic programming for on demand ride pooling"):
  with members:
    - "efficient ridesharing order dispatching with mean field multi agent reinforcement learning"
    - "parameter space noise for exploration"
    - "prioritized experience replay"
    - "deep reinforcement learning with double q learning"

Connected through hyperedge HE-8 (induced by source paper "demand prediction for electric vehicle sharing"):
  with members:
    - "bike flow prediction with multi graph convolutional networks"
    - "diffusion convolutional recurrent neural network data driven traffic forecasting"
    - "semi supervised classification with graph convolutional networks"
    - "spectral networks and locally connected networks on graphs"

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)