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 "graph embedded multi layer kernel extreme learning machine for one class classification or graph embedded multi layer kernel ridge regression for one class classification"):
  Abstract: A brain can detect outlier just by using only normal samples. Similarly, one-class classification (OCC) also uses only normal samples to train the model and trained model can be used for outlier detection. In this paper, a multi-layer architecture for OCC is proposed by stacking various Graph-Embedded Kernel Ridge Regression (KRR) based Auto-Encoders in a hierarchical fashion. These Auto-Encoders are formulated under two types of Graph-Embedding, namely, local and global variance-based embedding. This Graph-Embedding explores the relationship between samples and multi-layers of Auto-Encoder project the input features into new feature space. The last layer of this proposed architecture is Graph-Embedded regression-based one-class classifier. The Auto-Encoders use an unsupervised approach of learning and the final layer uses semi-supervised (trained by only positive samples and obtained closed-form solution) approach to learning. The proposed method is experimentally evaluated on 21 publicly available benchmark datasets. Experimental results verify the effectiveness of the proposed one-class classifiers over 11 existing state-of-the-art kernel-based one-class classifiers. Friedman test is also performed to verify the statistical significance of the claim of the superiority of the proposed one-class classifiers over the existing state-of-the-art methods. By using two types of Graph-Embedding, 4 variants of Graph-Embedded multi-layer KRR-based one-class classifier has been presented in this paper. All 4 variants performed better than the existing one-class classifiers in terms of various discussed criteria in this paper. Hence, it can be a viable alternative for OCC task. In the future, various other types of Auto-Encoders can be explored within proposed architecture.

Includes members:
  Member "on the construction of extreme learning machine for online and offline one class classification an expanded toolbox":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a stacked autoencoder neural network based automated feature extraction method for anomaly detection in on line condition monitoring")
  Member "deep learning in neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "π nets deep polynomial neural networks")
  Member "when is there a representer theorem vector versus matrix regularizers":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ockelm kernel extreme learning machine based one class classification using privileged information or koc kernel ridge regression or least square svm with zero bias based one class classification using privileged information")

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)