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: "silentphone inferring user unavailability based opportune moments to minimize call interruptions"
  Abstract: The increasing popularity of cell phones has made them the most personal and ubiquitous communication devices nowadays. Typically, the ringing notifications of mobile phones are used to inform the users about the incoming calls. However, the notifications of inappropriate incoming calls sometimes cause interruptions not only for the users but also the surrounding people. In this paper, we present a data-driven approach to infer the opportune moments for such phone call interruptions based on user's unavailability, i.e., when a user is unable to answer the incoming phone calls, by analyzing individual's past phone log data, and to discover the corresponding phone silent mode configuring rules for the purpose of minimizing call interruptions in an automated intelligent system. Experiments on the real mobile phone datasets show that our approach is able to identify the opportune moments for call interruptions and generates corresponding silent mode configuring rules by capturing the dominant behavior of individual users' at various times-of-the-day and days-of-the week.

Connected through hyperedge HE-1 (induced by source paper "a machine learning based robust prediction model for real life mobile phone data"):
  with members:
    - "research issues in mining user behavioral rules for context aware intelligent mobile applications"
    - "mining user behavioral rules from smartphone data through association analysis"
    - "individualized time series segmentation for mining mobile phone user behavior"
    - "mobile data science towards understanding data driven intelligent mobile applications"
    - "understanding the role of data centric social context in personalized mobile applications"
    - "an improved naive bayes classifier based noise detection technique for classifying user phone call behavior"

Connected through hyperedge HE-2 (induced by source paper "e miim an ensemble learning based context aware mobile telephony model for intelligent interruption management"):
  with members:
    - "a machine learning based robust prediction model for real life mobile phone data"
    - "research issues in mining user behavioral rules for context aware intelligent mobile applications"
    - "individualized time series segmentation for mining mobile phone user behavior"
    - "mobile data science towards understanding data driven intelligent mobile applications"
    - "understanding the role of data centric social context in personalized mobile applications"

Connected through hyperedge HE-3 (induced by source paper "calbehav a machine learning based personalized calendar behavioral model using time series smartphone data"):
  with members:
    - "a machine learning based robust prediction model for real life mobile phone data"
    - "mining user behavioral rules from smartphone data through association analysis"
    - "individualized time series segmentation for mining mobile phone user behavior"
    - "understanding the role of data centric social context in personalized mobile applications"
    - "an exploratory study of calendar use"

Connected through hyperedge HE-4 (induced by source paper "research issues in mining user behavioral rules for context aware intelligent mobile applications"):
  with members:
    - "mining user behavioral rules from smartphone data through association analysis"
    - "individualized time series segmentation for mining mobile phone user behavior"
    - "mobile data science towards understanding data driven intelligent mobile applications"
    - "understanding the role of data centric social context in personalized mobile applications"
    - "notimind utilizing responses to smart phone notifications as affective sensors"
    - "an improved naive bayes classifier based noise detection technique for classifying user phone call behavior"

Connected through hyperedge HE-5 (induced by source paper "mobile data science towards understanding data driven intelligent mobile applications"):
  with members:
    - "mining user behavioral rules from smartphone data through association analysis"
    - "understanding the role of data centric social context in personalized mobile applications"
    - "an improved naive bayes classifier based noise detection technique for classifying user phone call behavior"

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.CY(Computers and Society)