human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a machine learning based robust prediction model for real life mobile phone data"):
  Abstract: Real-life mobile phone data may contain noisy instances, which is a fundamental issue for building a prediction model with many potential negative consequences. The complexity of the inferred model may increase, may arise overfitting problem, and thereby the overall prediction accuracy of the model may decrease. In this paper, we address these issues and present a robust prediction model for real-life mobile phone data of individual users, in order to improve the prediction accuracy of the model. In our robust model, we first effectively identify and eliminate the noisy instances from the training dataset by determining a dynamic noise threshold using naive Bayes classifier and laplace estimator, which may differ from user-to-user according to their unique behavioral patterns. After that, we employ the most popular rule-based machine learning classification technique, i.e., decision tree, on the noise-free quality dataset to build the prediction model. Experimental results on the real-life mobile phone datasets (e.g., phone call log) of individual mobile phone users, show the effectiveness of our robust model in terms of precision, recall and f-measure.

Includes members:
  Member "research issues in mining user behavioral rules for context aware intelligent mobile applications":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "appspred predicting context aware smartphone apps using random forest learning")
  Member "mining user behavioral rules from smartphone data through association analysis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "appspred predicting context aware smartphone apps using random forest learning")
  Member "individualized time series segmentation for mining mobile phone user behavior":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "behavdt a behavioral decision tree learning to build user centric context aware predictive model")
  Member "mobile data science towards understanding data driven intelligent mobile applications":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "behavdt a behavioral decision tree learning to build user centric context aware predictive model")
  Member "understanding the role of data centric social context in personalized mobile applications":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "behavdt a behavioral decision tree learning to build user centric context aware predictive model")
  Member "silentphone inferring user unavailability based opportune moments to minimize call interruptions":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "e miim an ensemble learning based context aware mobile telephony model for intelligent interruption management")
  Member "an improved naive bayes classifier based noise detection technique for classifying user phone call behavior":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "appspred predicting context aware smartphone apps using random forest learning")

Question: Please tell me which class the source paper 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)