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: "mobile data science towards understanding data driven intelligent mobile applications"
  Abstract: Due to the popularity of smart mobile phones and context-aware technology, various contextual data relevant to users' diverse activities with mobile phones is available around us. This enables the study on mobile phone data and context-awareness in computing, for the purpose of building data-driven intelligent mobile applications, not only on a single device but also in a distributed environment for the benefit of end users. Based on the availability of mobile phone data, and the usefulness of data-driven applications, in this paper, we discuss about mobile data science that involves in collecting the mobile phone data from various sources and building data-driven models using machine learning techniques, in order to make dynamic decisions intelligently in various day-to-day situations of the users. For this, we first discuss the fundamental concepts and the potentiality of mobile data science to build intelligent applications. We also highlight the key elements and explain various key modules involving in the process of mobile data science. This article is the first in the field to draw a big picture, and thinking about mobile data science, and it's potentiality in developing various data-driven intelligent mobile applications. We believe this study will help both the researchers and application developers for building smart data-driven mobile applications, to assist the end mobile phone users in their daily activities.

Connected through hyperedge HE-1 (induced by source paper "behavdt a behavioral decision tree learning to build user centric context aware predictive model"):
  with members:
    - "e miim an ensemble learning based context aware mobile telephony model for intelligent interruption management"
    - "a machine learning based robust prediction model for real life mobile phone data"
    - "appspred predicting context aware smartphone apps using random forest learning"
    - "calbehav a machine learning based personalized calendar behavioral model using time series smartphone data"
    - "individualized time series segmentation for mining mobile phone user behavior"
    - "understanding the role of data centric social context in personalized mobile applications"
    - "estimating continuous distributions in bayesian classifiers"

Connected through hyperedge HE-2 (induced by source paper "appspred predicting context aware smartphone apps using random forest learning"):
  with members:
    - "e miim an ensemble learning based context aware mobile telephony model for intelligent interruption management"
    - "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"
    - "mining user behavioral rules from smartphone data through association analysis"
    - "individualized time series segmentation for mining mobile phone user behavior"
    - "an improved naive bayes classifier based noise detection technique for classifying user phone call behavior"
    - "estimating continuous distributions in bayesian classifiers"

Connected through hyperedge HE-3 (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"
    - "understanding the role of data centric social context in personalized mobile applications"
    - "silentphone inferring user unavailability based opportune moments to minimize call interruptions"
    - "an improved naive bayes classifier based noise detection technique for classifying user phone call behavior"

Connected through hyperedge HE-4 (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"
    - "understanding the role of data centric social context in personalized mobile applications"
    - "silentphone inferring user unavailability based opportune moments to minimize call interruptions"

Connected through hyperedge HE-5 (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"
    - "understanding the role of data centric social context in personalized mobile applications"
    - "silentphone inferring user unavailability based opportune moments to minimize call interruptions"
    - "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"

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)