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: "peak criterion for choosing gaussian kernel bandwidth in support vector data description"
  Abstract: Support Vector Data Description (SVDD) is a machine learning technique used for single class classification and outlier detection. SVDD formulation with kernel function provides a flexible boundary around data. The value of kernel function parameters affects the nature of data boundary. For example, it is observed that with Gaussian kernel, as the value of kernel bandwidth is lowered, the data boundary changes from spherical to wiggly. The spherical data boundary leads to underfitting and extremely wiggly data boundary leads to overfitting. In this paper we propose an empirical criteria to obtain a good value of Gaussian kernel bandwidth which provides a smooth boundary capturing the essential visual features of the data.

Connected through hyperedge HE-1 (induced by source paper "active learning of svdd hyperparameter values"):
  with members:
    - "an overview and a benchmark of active learning for outlier detection with one class classifiers"
    - "the mean and median criteria for kernel bandwidth selection for support vector data description"
    - "algorithms for learning kernels based on centered alignment"
    - "active learning from positive and unlabeled data"

Connected through hyperedge HE-2 (induced by source paper "a non parametric control chart for high frequency multivariate data"):
  with members:
    - "sampling method for fast training of support vector data description"

Connected through hyperedge HE-3 (induced by source paper "the mean and median criteria for kernel bandwidth selection for support vector data description"):
  with members:
    - "kernel bandwidth selection for svdd peak criterion approach for large data"

Connected through hyperedge HE-4 (induced by source paper "kernel bandwidth selection for svdd peak criterion approach for large data"):
  with members:
    - "sampling method for fast training of support vector data description"

Connected through hyperedge HE-5 (induced by source paper "a meta analysis of the anomaly detection problem"):
  with members:
    - "dendritic cells for anomaly detection"

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)