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: "research issues in mining user behavioral rules for context aware intelligent mobile applications"
  Abstract: Context-awareness in smart mobile applications is a growing area of study, because of it's intelligence in the applications. In order to build context-aware intelligent applications, mining contextual behavioral rules of individual smartphone users utilizing their phone log data is the key. However, to mine these rules, a number of issues, such as the quality of smartphone data, understanding the relevancy of contexts, discretization of continuous contextual data, discovery of useful behavioral rules of individuals and their ordering, knowledge-based interactive post-mining for semantic understanding, and dynamic updating and management of rules according to their present behavior, are investigated. In this paper, we briefly discuss these issues and their potential solution directions for mining individuals' behavioral rules, for the purpose of building various context-aware intelligent mobile applications. We also summarize a number of real-life rule-based applications that intelligently assist individual smartphone users according to their behavioral rules in their daily activities.

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"
    - "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"
    - "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:
    - "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"
    - "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 "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"
    - "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"

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.LG(Machine Learning)