human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "a non parametric control chart for high frequency multivariate data"):
  Abstract: Support Vector Data Description (SVDD) is a machine learning technique used for single class classification and outlier detection. A SVDD based K-chart was first introduced by Sun and Tsung [4]. K-chart provides an attractive alternative to the traditional control charts such as the Hotelling's T2 charts when the distribution of the underlying multivariate data is either non-normal or is unknown. But there are challenges when the K-chart is deployed in practice. The K-chart requires calculating the kernel distance of each new observation but there are no guidelines on how to interpret the kernel distance plot and draw inferences about shifts in process mean or changes in process variation. This limits the application of K-charts in big-data applications such as equipment health monitoring, where observations are generated at a very high frequency. In this scenario, the analyst using the K-chart is inundated with kernel distance results at a very high frequency, generally without any recourse for detecting presence of any assignable causes of variation. We propose a new SVDD based control chart, called a k T  chart, which addresses the challenges encountered when using a K-chart for big-data applications. The k T  charts can be used to track simultaneously process variation and central tendency.

Includes members:
  Member "peak criterion for choosing gaussian kernel bandwidth in support vector data description":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "active learning of svdd hyperparameter values")
  Member "sampling method for fast training of support vector data description":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "kernel bandwidth selection for svdd peak criterion approach for large data")

Question: Please tell me which class the source paper 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)