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: "where businesses thrive predicting the impact of the olympic games on local retailers through location based services data"
  Abstract: The Olympic Games are an important sporting event with notable consequences for the general economic landscape of the host city. Traditional economic assessments focus on the aggregated impact of the event on the national income, but fail to provide micro-scale insights on why local businesses will benefit from the increased activity during the Games. In this paper we provide a novel approach to modeling the impact of the Olympic Games on local retailers by analyzing a dataset mined from a large location-based social service, Foursquare. We hypothesize that the spatial positioning of businesses as well as the mobility trends of visitors are primary indicators of whether retailers will rise their popularity during the event. To confirm this we formulate a retail winners prediction task in the context of which we evaluate a set of geographic and mobility metrics. We find that the proximity to stadiums, the diversity of activity in the neighborhood, the nearby area sociability, as well as the probability of customer flows from and to event places such as stadiums and parks are all vital factors. Through supervised learning techniques we demonstrate that the success of businesses hinges on a combination of both geographic and mobility factors. Our results suggest that location-based social networks, where crowdsourced information about the dynamic interaction of users with urban spaces becomes publicly available, present an alternative medium to assess the economic impact of large scale events in a city.

Connected through hyperedge HE-1 (induced by source paper "predictive analysis on twitter techniques and applications"):
  with members:
    - "what s my age predicting twitter user s age using influential friend network and dbpedia"
    - "multiview deep learning for predicting twitter users location"
    - "on the challenges of sentiment analysis for dynamic events"
    - "early detection of promoted campaigns on social media"
    - "emojinet an open service and api for emoji sense discovery"
    - "location name extraction from targeted text streams using gazetteer based statistical language models"
    - "using social media to predict the future a systematic literature review"
    - "semi supervised approach to monitoring clinical depressive symptoms in social media"

Connected through hyperedge HE-2 (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"
    - "geo spotting mining online location based services for optimal retail store placement"

Connected through hyperedge HE-3 (induced by source paper "event organization 101 understanding latent factors of event popularity"):
  with members:
    - "the call of the crowd event participation in location based social services"

Connected through hyperedge HE-4 (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"
    - "the digital life of walkable streets"
    - "foodporn obesity patterns in culinary interactions"
    - "the shortest path to happiness recommending beautiful quiet and happy routes in the city"
    - "location prediction communities speak louder than friends"

Connected through hyperedge HE-5 (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"
    - "geo spotting mining online location based services for optimal retail store placement"
    - "multiple location profiling for users and relationships from social network and content"

Connected through hyperedge HE-6 (induced by source paper "topological properties and temporal dynamics of place networks in urban environments"):
  with members:
    - "geo spotting mining online location based services for optimal retail store placement"

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)