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: "measuring urban deprivation from user generated content"
  Abstract: Measuring socioeconomic deprivation of cities in an accurate and timely fashion has become a priority for governments around the world, as the massive urbanization process we are witnessing is causing high levels of inequalities which require intervention. Traditionally, deprivation indexes have been derived from census data, which is however very expensive to obtain, and thus acquired only every few years. Alternative computational methods have been proposed in recent years to automatically extract proxies of deprivation at a fine spatio-temporal level of granularity; however, they usually require access to datasets (e.g., call details records) that are not publicly available to governments and agencies. To remedy this, we propose a new method to automatically mine deprivation at a fine level of spatio-temporal granularity that only requires access to freely available user-generated content. More precisely, the method needs access to datasets describing what urban elements are present in the physical environment; examples of such datasets are Foursquare and OpenStreetMap. Using these datasets, we quantitatively describe neighborhoods by means of a metric, called Offering Advantage, that reflects which urban elements are distinctive features of each neighborhood. We then use that metric to (i) build accurate classifiers of urban deprivation and (ii) interpret the outcomes through thematic analysis. We apply the method to three UK urban areas of different scale and elaborate on the results in terms of precision and recall.

Connected through hyperedge HE-1 (induced by source paper "exploiting population activity dynamics to predict urban epidemiological incidence"):
  with members:
    - "mining large scale human mobility data for long term crime prediction"
    - "foodporn obesity patterns in culinary interactions"

Connected through hyperedge HE-2 (induced by source paper "why the world reads wikipedia beyond english speakers"):
  with members:
    - "why we read wikipedia"
    - "analysing timelines of national histories across wikipedia editions a comparative computational approach"
    - "what makes a link successful on wikipedia"
    - "cultures in community question answering"
    - "you are what you eat and drink identifying cultural boundaries by analyzing food drink habits in foursquare"
    - "detecting memory and structure in human navigation patterns using markov chain models of varying order"
    - "mining cross cultural relations from wikipedia a study of 31 european food cultures"

Connected through hyperedge HE-3 (induced by source paper "constructing urban tourism space digitally a study of airbnb listings in two berlin neighborhoods"):
  with members:
    - "service providers of the sharing economy who joins and who benefits"
    - "who benefits from the sharing economy of airbnb"

Connected through hyperedge HE-4 (induced by source paper "mining large scale human mobility data for long term crime prediction"):
  with members:
    - "once upon a crime towards crime prediction from demographics and mobile data"
    - "geo spotting mining online location based services for optimal retail store placement"

Connected through hyperedge HE-5 (induced by source paper "the tyranny of data the bright and dark sides of data driven decision making for social good"):
  with members:
    - "why should i trust you explaining the predictions of any classifier"
    - "smartphones in mental health detecting depressive and manic episodes"
    - "flooding through the lens of mobile phone activity"
    - "once upon a crime towards crime prediction from demographics and mobile data"
    - "daily stress recognition from mobile phone data weather conditions and individual traits"
    - "money walks a human centric study on the economics of personal mobile data"
    - "discrimination in online ad delivery"

Connected through hyperedge HE-6 (induced by source paper "emotions make cities live towards mapping emotions of older adults on urban space"):
  with members:
    - "a location based game for two generations teaching mobile technology to the elderly with the support of young volunteers"
    - "the shortest path to happiness recommending beautiful quiet and happy routes in the city"
    - "rule based emotion detection on social media putting tweets on plutchik s wheel"
    - "crowdsourcing a word emotion association lexicon"

Connected through hyperedge HE-7 (induced by source paper "cultural investment and urban socio economic development a geosocial network approach"):
  with members:
    - "topological properties and temporal dynamics of place networks in urban environments"
    - "where businesses thrive predicting the impact of the olympic games on local retailers through location based services data"
    - "geo spotting mining online location based services for optimal retail store placement"

Connected through hyperedge HE-8 (induced by source paper "community question answering platforms vs twitter for predicting characteristics of urban neighbourhoods"):
  with members:
    - "twitter mood predicts the stock market"

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)