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: "hoodsquare modeling and recommending neighborhoods in location based social networks"
  Abstract: Information garnered from activity on location-based social networks can be harnessed to characterize urban spaces and organize them into neighborhoods. We represent geographic points in the city using spatio-temporal information about Foursquare user check-ins and semantic information about places, with the goal of developing features to input into a novel neighborhood detection algorithm. The algorithm first employs a similarity metric that assesses the homogeneity of a geographic area, and then with a simple mechanism of geographic navigation, it detects the boundaries of a city's neighborhoods. The models and algorithms devised are subsequently integrated into a publicly available, map-based tool named Hood square that allows users to explore activities and neighborhoods in cities around the world. Finally, we evaluate Hood square in the context of are commendation application where user profiles are matched to urban neighborhoods. By comparing with a number of baselines, we demonstrate how Hood square can be used to accurately predict the home neighborhood of Twitter users. We also show that we are able to suggest neighborhoods geographically constrained in size, a desirable property in mobile recommendation scenarios for which geographical precision is key.

Connected through hyperedge HE-1 (induced by source paper "sensing and modeling human behavior using social media and mobile data"):
  with members:
    - "a multilayer approach to multiplexity and link prediction in online geo social networks"
    - "anticipatory mobile computing a survey of the state of the art and research challenges"
    - "investigating causality in human behavior from smartphone sensor data a quasi experimental approach"
    - "keep your friends close and your facebook friends closer a multiplex network approach to the analysis of offline and online social ties"
    - "you are what you eat and drink identifying cultural boundaries by analyzing food drink habits in foursquare"
    - "predicting influential users in online social networks"

Connected through hyperedge HE-2 (induced by source paper "extracting patterns of urban activity from geotagged social data"):
  with members:
    - "geo sage a geographical sparse additive generative model for spatial item recommendation"
    - "on the accuracy of hyper local geotagging of social media content"

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.CY(Computers and Society)