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: "estimating continuous distributions in bayesian classifiers"
  Abstract: When modeling a probability distribution with a Bayesian network, we are faced with the problem of how to handle continuous variables. Most previous work has either solved the problem by discretizing, or assumed that the data are generated by a single Gaussian. In this paper we abandon the normality assumption and instead use statistical methods for nonparametric density estimation. For a naive Bayesian classifier, we present experimental results on a variety of natural and artificial domains, comparing two methods of density estimation: assuming normality and modeling each conditional distribution with a single Gaussian; and using nonparametric kernel density estimation. We observe large reductions in error on several natural and artificial data sets, which suggests that kernel estimation is a useful tool for learning Bayesian models.

Connected through hyperedge HE-1 (induced by source paper "needmining identifying micro blog data containing customer needs"):
  with members:
    - "a bayesian method for constructing bayesian belief networks from databases"
    - "smote synthetic minority over sampling technique"

Connected through hyperedge HE-2 (induced by source paper "finding the mode of a kernel density estimate"):
  with members:
    - "discrepancy coresets and sketches in machine learning"
    - "performance of johnson lindenstrauss transform for k means and k medians clustering"
    - "optimal terminal dimensionality reduction in euclidean space"
    - "improved coresets for kernel density estimates"
    - "coresets for kernel regression"
    - "dimensionality reduction for k means clustering and low rank approximation"
    - "union of random minkowski sums and network vulnerability analysis"
    - "geometric inference on kernel density estimates"

Connected through hyperedge HE-3 (induced by source paper "do you really follow them automatic detection of credulous twitter users"):
  with members:
    - "influence of fake news in twitter during the 2016 us presidential election"
    - "arming the public with artificial intelligence to counter social bots"
    - "the spread of low credibility content by social bots"
    - "mean birds detecting aggression and bullying on twitter"
    - "the paradigm shift of social spambots evidence theories and tools for the arms race"
    - "online human bot interactions detection estimation and characterization"
    - "the rise of social bots"
    - "fame for sale"

Connected through hyperedge HE-4 (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"
    - "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-5 (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"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "sub linear race sketches for approximate kernel density estimation on streaming data"):
  with members:
    - "discrepancy coresets and sketches in machine learning"
    - "race sub linear memory sketches for approximate near neighbor search on streaming data"
    - "hashing based estimators for kernel density in high dimensions"
    - "multi resolution hashing for fast pairwise summations"
    - "privacy via the johnson lindenstrauss transform"
    - "super samples from kernel herding"

Connected through hyperedge HE-7 (induced by source paper "detecting vietnamese opinion spam"):
  with members:
    - "finding deceptive opinion spam by any stretch of the imagination"
    - "how opinions are received by online communities a case study on amazon com helpfulness votes"
    - "machine learning in automated text categorization"

Connected through hyperedge HE-8 (induced by source paper "predicting failures in multi tier distributed systems"):
  with members:
    - "a platform for automating chaos experiments"
    - "large scale complex it systems"

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)