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: "lacboost and fisherboost optimally building cascade classifiers"
  Abstract: Object detection is one of the key tasks in computer vision. The cascade framework of Viola and Jones has become the de facto standard. A classifier in each node of the cascade is required to achieve extremely high detection rates, instead of low overall classification error. Although there are a few reported methods addressing this requirement in the context of object detection, there is no a principled feature selection method that explicitly takes into account this asymmetric node learning objective. We provide such a boosting algorithm in this work. It is inspired by the linear asymmetric classifier (LAC) of Wu et al. in that our boosting algorithm optimizes a similar cost function. The new totally-corrective boosting algorithm is implemented by the column generation technique in convex optimization. Experimental results on face detection suggest that our proposed boosting algorithms can improve the state-of-the-art methods in detection performance.

Connected through hyperedge HE-1 (induced by source paper "training effective node classifiers for cascade classification"):
  with members:
    - "on the dual formulation of boosting algorithms"
    - "efficiently learning a detection cascade with sparse eigenvectors"
    - "boosting through optimization of margin distributions"

Connected through hyperedge HE-2 (induced by source paper "optimally training a cascade classifier"):
  with members:
    - "asymmetric totally corrective boosting for real time object detection"
    - "on the dual formulation of boosting algorithms"
    - "efficiently learning a detection cascade with sparse eigenvectors"

Connected through hyperedge HE-3 (induced by source paper "asymmetric totally corrective boosting for real time object detection"):
  with members:
    - "on the dual formulation of boosting algorithms"

Connected through hyperedge HE-4 (induced by source paper "totally corrective boosting for regularized risk minimization"):
  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.CV(Computer Vision and Pattern Recognition)