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 "calbehav a machine learning based personalized calendar behavioral model using time series smartphone data"):
  Abstract: The electronic calendar is a valuable resource nowadays for managing our daily life appointments or schedules, also known as events, ranging from professional to highly personal. Researchers have studied various types of calendar events to predict smartphone user behavior for incoming mobile communications. However, these studies typically do not take into account behavioral variations between individuals. In the real world, smartphone users can differ widely from each other in how they respond to incoming communications during their scheduled events. Moreover, an individual user may respond the incoming communications differently in different contexts subject to what type of event is scheduled in her personal calendar. Thus, a static calendar-based behavioral model for individual smartphone users does not necessarily reflect their behavior to the incoming communications. In this paper, we present a machine learning based context-aware model that is personalized and dynamically identifies individual's dominant behavior for their scheduled events using logged time-series smartphone data, and shortly name as ``CalBehav''. The experimental results based on real datasets from calendar and phone logs, show that this data-driven personalized model is more effective for intelligently managing the incoming mobile communications compared to existing calendar-based approaches.

Includes members:
  Member "a machine learning based robust prediction model for real life mobile phone data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "behavdt a behavioral decision tree learning to build user centric context aware predictive model")
  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 "understanding the role of data centric social context in personalized 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 "silentphone inferring user unavailability based opportune moments to minimize call interruptions":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a machine learning based robust prediction model for real life mobile phone data")
  Member "an exploratory study of calendar use":
    also connected through other hyperedges:
      (none)

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.CY(Computers and Society)