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: "boosting through optimization of margin distributions"
  Abstract: Boosting has attracted much research attention in the past decade. The success of boosting algorithms may be interpreted in terms of the margin theory. Recently it has been shown that generalization error of classifiers can be obtained by explicitly taking the margin distribution of the training data into account. Most of the current boosting algorithms in practice usually optimizes a convex loss function and do not make use of the margin distribution. In this work we design a new boosting algorithm, termed margin-distribution boosting (MDBoost), which directly maximizes the average margin and minimizes the margin variance simultaneously. This way the margin distribution is optimized. A totally-corrective optimization algorithm based on column generation is proposed to implement MDBoost. Experiments on UCI datasets show that MDBoost outperforms AdaBoost and LPBoost in most cases.

Connected through hyperedge HE-1 (induced by source paper "from kernel machines to ensemble learning"):
  with members:
    - "on the dual formulation of boosting algorithms"

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"
    - "efficiently learning a detection cascade with sparse eigenvectors"

Connected through hyperedge HE-3 (induced by source paper "a scalable stage wise approach to large margin multi class loss based boosting"):
  with members:
    - "multiclass learning approaches a theoretical comparison with implications"
    - "on the dual formulation of boosting algorithms"

Connected through hyperedge HE-4 (induced by source paper "randomboost simplified multi class boosting through randomization"):
  with members:
    - "on the dual formulation of boosting algorithms"
    - "solving multiclass learning problems via error correcting output codes"

Connected through hyperedge HE-5 (induced by source paper "margin distribution controlled boosting"):
  with members:
    - "on the dual formulation of boosting algorithms"

Connected through hyperedge HE-6 (induced by source paper "incremental training of a detector using online sparse eigendecomposition"):
  with members:
    - "on the dual formulation of boosting algorithms"

Connected through hyperedge HE-7 (induced by source paper "efficiently learning a detection cascade with sparse eigenvectors"):
  with members:
    - "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.LG(Machine Learning)