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: "the digital life of walkable streets"
  Abstract: Walkability has many health, environmental, and economic benefits. That is why web and mobile services have been offering ways of computing walkability scores of individual street segments. Those scores are generally computed from survey data and manual counting (of even trees). However, that is costly, owing to the high time, effort, and financial costs. To partly automate the computation of those scores, we explore the possibility of using the social media data of Flickr and Foursquare to automatically identify safe and walkable streets. We find that unsafe streets tend to be photographed during the day, while walkable streets are tagged with walkability-related keywords. These results open up practical opportunities (for, e.g., room booking services, urban route recommenders, and real-estate sites) and have theoretical implications for researchers who might resort to the use social media data to tackle previously unanswered questions in the area of walkability.

Connected through hyperedge HE-1 (induced by source paper "facelift a transparent deep learning framework to beautify urban scenes"):
  with members:
    - "augmented reality meets computer vision efficient data generation for urban driving scenes"
    - "disentangled person image generation"
    - "unsupervised cross domain image generation"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "synthesizing the preferred inputs for neurons in neural networks via deep generator networks"
    - "generating images with perceptual similarity metrics based on deep networks"
    - "are safer looking neighborhoods more lively a multimodal investigation into urban life"
    - "deep learning the city quantifying urban perception at a global scale"

Connected through hyperedge HE-2 (induced by source paper "to stay or to leave churn prediction for urban migrants in the initial period"):
  with members:
    - "urban dreams of migrants a case study of migrant integration in shanghai"
    - "motifs in temporal networks"
    - "the death and life of great italian cities a mobile phone data perspective"
    - "twitter as a source of global mobility patterns for social good"
    - "assembling thefacebook using heterogeneity to understand online social network assembly"
    - "all who wander on the prevalence and characteristics of multi community engagement"
    - "from amateurs to connoisseurs modeling the evolution of user expertise through online reviews"

Connected through hyperedge HE-3 (induced by source paper "a selfie is worth a thousand words mining personal patterns behind user selfie posting behaviours"):
  with members:
    - "what the language you tweet says about your occupation"
    - "on diffusion restricted social network a measurement study of wechat moments"
    - "deep residual learning for image recognition"
    - "identifying relevant messages in a twitter based citizen channel for natural disaster situations"
    - "the lifecycle and cascade of wechat social messaging groups"
    - "a picture tells a thousand words about you user interest profiling from user generated visual content"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-4 (induced by source paper "mobinsight a framework using semantic neighborhood features for localized interpretations of urban mobility"):
  with members:
    - "urban pulse capturing the rhythm of cities"
    - "the death and life of great italian cities a mobile phone data perspective"
    - "not at home on the range peer production and the urban rural divide"
    - "are safer looking neighborhoods more lively a multimodal investigation into urban life"
    - "the shortest path to happiness recommending beautiful quiet and happy routes in the city"
    - "exploring universal patterns in human home work commuting from mobile phone data"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-5 (induced by source paper "urban dreams of migrants a case study of migrant integration in shanghai"):
  with members:
    - "twitter as a source of global mobility patterns for social good"

Connected through hyperedge HE-6 (induced by source paper "mining the social media data for a bottom up evaluation of walkability"):
  with members:
    - "the shortest path to happiness recommending beautiful quiet and happy routes in the city"

Connected through hyperedge HE-7 (induced by source paper "understanding and mapping natural beauty"):
  with members:
    - "a unified model for near and remote sensing"
    - "planet photo geolocation with convolutional neural networks"
    - "horizon lines in the wild"
    - "predicting ground level scene layout from aerial imagery"
    - "understanding image virality"
    - "wide area image geolocalization with aerial reference imagery"
    - "we are humor beings understanding and predicting visual humor"
    - "the shortest path to happiness recommending beautiful quiet and happy routes in the city"

Connected through hyperedge HE-8 (induced by source paper "quantifying location sociality"):
  with members:
    - "smelly maps the digital life of urban smellscapes"
    - "measuring urban deprivation from user generated content"
    - "dawn of the selfie era the whos wheres and hows of selfies on instagram"
    - "topological properties and temporal dynamics of place networks in urban environments"
    - "foodporn obesity patterns in culinary interactions"
    - "the shortest path to happiness recommending beautiful quiet and happy routes in the city"
    - "where businesses thrive predicting the impact of the olympic games on local retailers through location based services data"
    - "location prediction communities speak louder than friends"

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)