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: "geo spotting mining online location based services for optimal retail store placement"
  Abstract: The problem of identifying the optimal location for a new retail store has been the focus of past research, especially in the field of land economy, due to its importance in the success of a business. Traditional approaches to the problem have factored in demographics, revenue and aggregated human flow statistics from nearby or remote areas. However, the acquisition of relevant data is usually expensive. With the growth of location-based social networks, fine grained data describing user mobility and popularity of places has recently become attainable.   In this paper we study the predictive power of various machine learning features on the popularity of retail stores in the city through the use of a dataset collected from Foursquare in New York. The features we mine are based on two general signals: geographic, where features are formulated according to the types and density of nearby places, and user mobility, which includes transitions between venues or the incoming flow of mobile users from distant areas. Our evaluation suggests that the best performing features are common across the three different commercial chains considered in the analysis, although variations may exist too, as explained by heterogeneities in the way retail facilities attract users. We also show that performance improves significantly when combining multiple features in supervised learning algorithms, suggesting that the retail success of a business may depend on multiple factors.

Connected through hyperedge HE-1 (induced by source paper "under the hood of membership inference attacks on aggregate location time series"):
  with members:
    - "on the lack of location privacy in crowdsourcing applications"
    - "trajectory recovery from ash user privacy is not preserved in aggregated mobility data"
    - "what does the crowd say about you evaluating aggregation based location privacy"
    - "differentially private publication of location entropy"
    - "membership inference attacks against machine learning models"
    - "privacy friendly mobility analytics using aggregate location data"
    - "on sampling anonymization and differential privacy or k anonymization meets differential privacy"

Connected through hyperedge HE-2 (induced by source paper "towards business partnership recommendation using user opinion on facebook"):
  with members:
    - "community discovery in dynamic networks a survey"
    - "gender matters analyzing global cultural gender preferences for venues using social sensing"
    - "chatty maps constructing sound maps of urban areas from social media data"
    - "where is the goldmine finding promising business locations through facebook data analytics"
    - "you are what you eat and drink identifying cultural boundaries by analyzing food drink habits in foursquare"

Connected through hyperedge HE-3 (induced by source paper "mining large scale human mobility data for long term crime prediction"):
  with members:
    - "measuring urban deprivation from user generated content"
    - "once upon a crime towards crime prediction from demographics and mobile data"

Connected through hyperedge HE-4 (induced by source paper "predicting the temporal activity patterns of new venues"):
  with members:
    - "you are what you eat and drink identifying cultural boundaries by analyzing food drink habits in foursquare"

Connected through hyperedge HE-5 (induced by source paper "systems applications of social networks"):
  with members:
    - "a survey of signed network mining in social media"
    - "the architecture of innovation tracking face to face interactions with ubicomp technologies"
    - "social bootstrapping how pinterest and last fm social communities benefit by borrowing links from facebook"
    - "social turing tests crowdsourcing sybil detection"
    - "explore what if scenarios with song social network write generator"
    - "uncovering social network sybils in the wild"
    - "empirical comparison of algorithms for network community detection"
    - "supervised random walks predicting and recommending links in social networks"

Connected through hyperedge HE-6 (induced by source paper "cultural investment and urban socio economic development a geosocial network approach"):
  with members:
    - "measuring urban deprivation from user generated content"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "where is the goldmine finding promising business locations through facebook data analytics"):
  with members:
    - "a business zone recommender system based on facebook and urban planning data"
    - "where businesses thrive predicting the impact of the olympic games on local retailers through location based services data"
    - "multiple location profiling for users and relationships from social network and content"

Connected through hyperedge HE-8 (induced by source paper "store location selection via mining search query logs of baidu maps"):
  with members:
    - "consensus answers for queries over probabilistic databases"

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)