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: "social network analytics for churn prediction in telco"
  Abstract: Comparison of Social Networks Analytics methods for predicting churn in telco.Ranking of 24 relational learners with respect to predictive performance.Collective inferencing does not improve the performance of relational classifiers.The best models apply a classifier with network features and relational learner scores.Network construction matters for model performance. Social network analytics methods are being used in the telecommunication industry to predict customer churn with great success. In particular it has been shown that relational learners adapted to this specific problem enhance the performance of predictive models. In the current study we benchmark different strategies for constructing a relational learner by applying them to a total of eight distinct call-detail record datasets, originating from telecommunication organizations across the world. We statistically evaluate the effect of relational classifiers and collective inference methods on the predictive power of relational learners, as well as the performance of models where relational learners are combined with traditional methods of predicting customer churn in the telecommunication industry. Finally we investigate the effect of network construction on model performance; our findings imply that the definition of edges and weights in the network does have an impact on the results of the predictive models. As a result of the study, the best configuration is a non-relational learner enriched with network variables, without collective inference, using binary weights and undirected networks. In addition, we provide guidelines on how to apply social networks analytics for churn prediction in the telecommunication industry in an optimal way, ranging from network architecture to model building and evaluation.

Connected through hyperedge HE-1 (induced by source paper "snel sql native execution for llvm"):
  with members:
    - "the value of big data for credit scoring enhancing financial inclusion using mobile phone data and social network analytics"
    - "uncovering the spread of chagas disease in argentina and mexico"
    - "comparison of feature extraction methods and predictors for income inference"
    - "inference of users demographic attributes based on homophily in communication networks"
    - "social events in a time varying mobile phone graph"
    - "the city pulse of buenos aires"
    - "prepaid or postpaid that is the question novel methods of subscription type prediction in mobile phone services"
    - "analyzing the spread of chagas disease with mobile phone data"

Connected through hyperedge HE-2 (induced by source paper "credit scoring for good enhancing financial inclusion with smartphone based microlending"):
  with members:
    - "the value of big data for credit scoring enhancing financial inclusion using mobile phone data and social network analytics"

Connected through hyperedge HE-3 (induced by source paper "the value of big data for credit scoring enhancing financial inclusion using mobile phone data and social network analytics"):
  with members:
    - "socioeconomic correlations and stratification in social communication networks"
    - "a study of age and gender seen through mobile phone usage patterns in mexico"

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)