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: "regularized singular value decomposition and application to recommender system"
  Abstract: Singular value decomposition (SVD) is the mathematical basis of principal component analysis (PCA). Together, SVD and PCA are one of the most widely used mathematical formalism/decomposition in machine learning, data mining, pattern recognition, artificial intelligence, computer vision, signal processing, etc. In recent applications, regularization becomes an increasing trend. In this paper, we present a regularized SVD (RSVD), present an efficient computational algorithm, and provide several theoretical analysis. We show that although RSVD is non-convex, it has a closed-form global optimal solution. Finally, we apply RSVD to the application of recommender system and experimental result show that RSVD outperforms SVD significantly.

Connected through hyperedge HE-1 (induced by source paper "generative adversarial networks for failure prediction"):
  with members:
    - "a cost sensitive deep belief network for imbalanced classification"
    - "minimal support vector machine"
    - "predicting rapid fire growth flashover using conditional generative adversarial networks"
    - "camera style adaptation for person re identification"
    - "controllable generative adversarial network"
    - "a bayesian data augmentation approach for learning deep models"
    - "unsupervised pixel level domain adaptation with generative adversarial networks"
    - "a harmonic mean linear discriminant analysis for robust image classification"

Connected through hyperedge HE-2 (induced by source paper "manufacturing dispatching using reinforcement and transfer learning"):
  with members:
    - "minimal support vector machine"
    - "cross domain transfer in reinforcement learning using target apprentice"
    - "a harmonic mean linear discriminant analysis for robust image classification"
    - "accelerating deep learning with shrinkage and recall"
    - "kernel alignment inspired linear discriminant analysis"
    - "analysis and modeling of social influence in high performance computing workloads"

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)