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: "efficiently learning a detection cascade with sparse eigenvectors"
  Abstract: In this work, we first show that feature selection methods other than boosting can also be used for training an efficient object detector. In particular, we introduce Greedy Sparse Linear Discriminant Analysis (GSLDA) \cite{Moghaddam2007Fast} for its conceptual simplicity and computational efficiency; and slightly better detection performance is achieved compared with \cite{Viola2004Robust}. Moreover, we propose a new technique, termed Boosted Greedy Sparse Linear Discriminant Analysis (BGSLDA), to efficiently train a detection cascade. BGSLDA exploits the sample re-weighting property of boosting and the class-separability criterion of GSLDA.

Connected through hyperedge HE-1 (induced by source paper "a decomposition algorithm for sparse generalized eigenvalue problem"):
  with members:
    - "efficient algorithms for large scale generalized eigenvector computation and canonical correlation analysis"
    - "efficient and non convex coordinate descent for symmetric nonnegative matrix factorization"
    - "sparsity constrained nonlinear optimization optimality conditions and algorithms"
    - "incremental training of a detector using online sparse eigendecomposition"
    - "optimal solutions for sparse principal component analysis"
    - "a direct formulation for sparse pca using semidefinite programming"

Connected through hyperedge HE-2 (induced by source paper "training effective node classifiers for cascade classification"):
  with members:
    - "lacboost and fisherboost optimally building cascade classifiers"
    - "on the dual formulation of boosting algorithms"
    - "boosting through optimization of margin distributions"

Connected through hyperedge HE-3 (induced by source paper "the sparse principal component of a constant rank matrix"):
  with members:
    - "sparse principal component of a rank deficient matrix"
    - "optimal solutions for sparse principal component analysis"
    - "a direct formulation for sparse pca using semidefinite programming"

Connected through hyperedge HE-4 (induced by source paper "asymmetric pruning for learning cascade detectors"):
  with members:
    - "asymmetric totally corrective boosting for real time object detection"

Connected through hyperedge HE-5 (induced by source paper "optimally training a cascade classifier"):
  with members:
    - "asymmetric totally corrective boosting for real time object detection"
    - "lacboost and fisherboost optimally building cascade classifiers"
    - "on the dual formulation of boosting algorithms"

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.MM(Multimedia)