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: "double base asymmetric adaboost"
  Abstract: Based on the use of different exponential bases to define class-dependent error bounds, a new and highly efficient asymmetric boosting scheme, coined as AdaBoostDB (Double-Base), is proposed. Supported by a fully theoretical derivation procedure, unlike most of the other approaches in the literature, our algorithm preserves all the formal guarantees and properties of original (cost-insensitive) AdaBoost, similarly to the state-of-the-art Cost-Sensitive AdaBoost algorithm. However, the key advantage of AdaBoostDB is that our novel derivation scheme enables an extremely efficient conditional search procedure, dramatically improving and simplifying the training phase of the algorithm. Experiments, both over synthetic and real datasets, reveal that AdaBoostDB is able to save over 99% training time with regard to Cost-Sensitive AdaBoost, providing the same cost-sensitive results. This computational advantage of AdaBoostDB can make a difference in problems managing huge pools of weak classifiers in which boosting techniques are commonly used.

Connected through hyperedge HE-1 (induced by source paper "boosting with lexicographic programming addressing class imbalance without cost tuning"):
  with members:
    - "rethinking the inception architecture for computer vision"
    - "spectral unmixing via data guided sparsity"
    - "on the dual formulation of boosting algorithms"

Connected through hyperedge HE-2 (induced by source paper "classification with boosting of extreme learning machine over arbitrarily partitioned data"):
  with members:
    - "cloudsvm training an svm classifier in cloud computing systems"

Connected through hyperedge HE-3 (induced by source paper "untangling adaboost based cost sensitive classification part ii empirical analysis"):
  with members:
    - "untangling adaboost based cost sensitive classification part i theoretical perspective"
    - "learning when training data are costly the effect of class distribution on tree induction"
    - "popular ensemble methods an empirical study"

Connected through hyperedge HE-4 (induced by source paper "untangling adaboost based cost sensitive classification part i theoretical perspective"):
  with members:
    - "untangling adaboost based cost sensitive classification part ii empirical analysis"
    - "learning when training data are costly the effect of class distribution on tree induction"
    - "popular ensemble methods an empirical study"

Connected through hyperedge HE-5 (induced by source paper "revisiting adaboost for cost sensitive classification part ii empirical analysis"):
  with members:
    - "shedding light on the asymmetric learning capability of adaboost"
    - "learning when training data are costly the effect of class distribution on tree induction"
    - "popular ensemble methods an empirical study"

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)