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: "individualized time series segmentation for mining mobile phone user behavior"
  Abstract: Mobile phones can record individual's daily behavioral data as a time-series. In this paper, we present an effective time-series segmentation technique that extracts optimal time segments of individual's similar behavioral characteristics utilizing their mobile phone data. One of the determinants of an individual's behavior is the various activities undertaken at various times-of-the-day and days-of-the-week. In many cases, such behavior will follow temporal patterns. Currently, researchers use either equal or unequal interval-based segmentation of time for mining mobile phone users' behavior. Most of them take into account static temporal coverage of 24-h-a-day and few of them take into account the number of incidences in time-series data. However, such segmentations do not necessarily map to the patterns of individual user activity and subsequent behavior because of not taking into account the diverse behaviors of individuals over time-of-the-week. Therefore, we propose a behavior-oriented time segmentation (BOTS) technique that takes into account not only the temporal coverage of the week but also the number of incidences of diverse behaviors dynamically for producing similar behavioral time segments over the week utilizing time-series data. Experiments on the real mobile phone datasets show that our proposed segmentation technique better captures the user's dominant behavior at various times-of-the-day and days-of-the-week enabling the generation of high confidence temporal rules in order to mine individual mobile phone users' behavior.

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"
    - "mobile data science towards understanding data driven intelligent mobile applications"
    - "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"
    - "mobile data science towards understanding data driven intelligent mobile applications"
    - "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"
    - "mobile data science towards understanding data driven intelligent mobile applications"
    - "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"
    - "mobile data science towards understanding data driven intelligent mobile applications"
    - "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 "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"
    - "understanding the role of data centric social context in personalized mobile applications"
    - "silentphone inferring user unavailability based opportune moments to minimize call interruptions"
    - "an exploratory study of calendar use"

Connected through hyperedge HE-6 (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"
    - "mobile data science towards understanding data driven intelligent mobile applications"
    - "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)