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: "mining user behavioral rules from smartphone data through association analysis"
  Abstract: The increasing popularity of smart mobile phones and their powerful sensing capabilities have enabled the collection of rich contextual information and mobile phone usage records through the device logs. This paper formulates the problem of mining behavioral association rules of individual mobile phone users utilizing their smartphone data. Association rule learning is the most popular technique to discover rules utilizing large datasets. However, it is well-known that a large proportion of association rules generated are redundant. This redundant production makes not only the rule-set unnecessarily large but also makes the decision making process more complex and ineffective. In this paper, we propose an approach that effectively identifies the redundancy in associations and extracts a concise set of behavioral association rules that are non-redundant. The effectiveness of the proposed approach is examined by considering the real mobile phone datasets of individual users.

Connected through hyperedge HE-1 (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"
    - "individualized time series segmentation for mining mobile phone user behavior"
    - "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-2 (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"
    - "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"
    - "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-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"
    - "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 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:
    - "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"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "mobile data science towards understanding data driven intelligent mobile applications"):
  with members:
    - "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"

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.DB(Databases)