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 decomposition algorithm for sparse generalized eigenvalue problem"):
  Abstract: Sparse generalized eigenvalue problem arises in a number of standard and modern statistical learning models, including sparse principal component analysis, sparse Fisher discriminant analysis and sparse canonical correlation analysis. However, this problem is difficult to solve since it is NP-hard. In this paper, we consider a new decomposition method to tackle this problem. Specifically, we use random or/and swapping strategies to find a working set and perform global combinatorial search over the small subset of variables. We consider a bisection search method and a coordinate descent method for solving the quadratic fractional programming subproblem. In addition, we provide theoretical analysis for the proposed decomposition algorithm. Extensive experiments on both real-world and synthetic data sets have shown that the proposed method achieves state-of-the-art performance in terms of accuracy.

Includes members:
  Member "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "variational optimization on lie groups with examples of leading generalized eigenvalue problems")
  Member "efficient and non convex coordinate descent for symmetric nonnegative matrix factorization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "synchronisation of partial multi matchings via non negative factorisations")
  Member "sparsity constrained nonlinear optimization optimality conditions and algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient relaxed gradient support pursuit for sparsity constrained non convex optimization")
  Member "incremental training of a detector using online sparse eigendecomposition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "large margin learning of compact binary image encodings")
  Member "efficiently learning a detection cascade with sparse eigenvectors":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "training effective node classifiers for cascade classification")
  Member "optimal solutions for sparse principal component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a unified framework for data poisoning attack to graph based semi supervised learning")
  Member "a direct formulation for sparse pca using semidefinite programming":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "supervised quantile normalization for low rank matrix factorization")

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.NA(Numerical Analysis)