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 call of the crowd event participation in location based social services"
  Abstract: Understanding the social and behavioral forces behind event participation is not only interesting from the viewpoint of social science, but also has important applications in the design of personalized event recommender systems. This paper takes advantage of data from a widely used location-based social network, Foursquare, to analyze event patterns in three metropolitan cities. We put forward several hypotheses on the motivating factors of user participation and confirm that social aspects play a major role in determining the likelihood of a user to participate in an event. While an explicit social filtering signal accounting for whether friends are attending dominates the factors, the popularity of an event proves to also be a strong attractor. Further, we capture an implicit social signal by performing random walks in a high dimensional graph that encodes the place type preferences of friends and that proves especially suited to identify relevant niche events for users. Our findings on the extent to which the various temporal, spatial and social aspects underlie users' event preferences lead us to further hypothesize that a combination of factors better models users' event interests. We verify this through a supervised learning framework. We show that for one in three users in London and one in five users in New York and Chicago it identifies the exact event the user would attend among the pool of suggestions.

Connected through hyperedge HE-1 (induced by source paper "gevr an event venue recommendation system for groups of mobile users"):
  with members:
    - "this is why we play characterizing online fan communities of the nba teams"
    - "understanding group event scheduling via the outwithfriendz mobile application"

Connected through hyperedge HE-2 (induced by source paper "social influence deep learning for human behavior prediction"):
  with members:
    - "on the social influence in human behavior physical homophily and social communities"
    - "neural collaborative filtering"
    - "deep learning in neural networks"
    - "deep residual learning for image recognition"
    - "predicting adoption probabilities in social networks"

Connected through hyperedge HE-3 (induced by source paper "gender matters analyzing global cultural gender preferences for venues using social sensing"):
  with members:
    - "it s a man s wikipedia assessing gender inequality in an online encyclopedia"
    - "first women second sex gender bias in wikipedia"
    - "you are what you eat and drink identifying cultural boundaries by analyzing food drink habits in foursquare"
    - "gender asymmetries in reality and fiction the bechdel test of social media"
    - "uncovering social network sybils in the wild"

Connected through hyperedge HE-4 (induced by source paper "event organization 101 understanding latent factors of event popularity"):
  with members:
    - "where businesses thrive predicting the impact of the olympic games on local retailers through location based services data"

Connected through hyperedge HE-5 (induced by source paper "inferring unusual crowd events from mobile phone call detail records"):
  with members:
    - "mining users significant driving routes with low power sensors"
    - "discovery of convoys in trajectory 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)