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: "meta des h a dynamic ensemble selection technique using meta learning and a dynamic weighting approach"
  Abstract: In Dynamic Ensemble Selection (DES) techniques, only the most competent classifiers are selected to classify a given query sample. Hence, the key issue in DES is how to estimate the competence of each classifier in a pool to select the most competent ones. In order to deal with this issue, we proposed a novel dynamic ensemble selection framework using meta-learning, called META-DES. The framework is divided into three steps. In the first step, the pool of classifiers is generated from the training data. In the second phase the meta-features are computed using the training data and used to train a meta-classifier that is able to predict whether or not a base classifier from the pool is competent enough to classify an input instance. In this paper, we propose improvements to the training and generalization phase of the META-DES framework. In the training phase, we evaluate four different algorithms for the training of the meta-classifier. For the generalization phase, three combination approaches are evaluated: Dynamic selection, where only the classifiers that attain a certain competence level are selected; Dynamic weighting, where the meta-classifier estimates the competence of each classifier in the pool, and the outputs of all classifiers in the pool are weighted based on their level of competence; and a hybrid approach, in which first an ensemble with the most competent classifiers is selected, after which the weights of the selected classifiers are estimated in order to be used in a weighted majority voting scheme. Experiments are carried out on 30 classification datasets. Experimental results demonstrate that the changes proposed in this paper significantly improve the recognition accuracy of the system in several datasets.

Connected through hyperedge HE-1 (induced by source paper "evaluating competence measures for dynamic regressor selection"):
  with members:
    - "meta des"
    - "cixl2 a crossover operator for evolutionary algorithms based on population features"

Connected through hyperedge HE-2 (induced by source paper "meta des oracle meta learning and feature selection for ensemble selection"):
  with members:
    - "a deep analysis of the meta des framework for dynamic selection of ensemble of classifiers"
    - "meta des"
    - "on meta learning for dynamic ensemble selection"
    - "a method for dynamic ensemble selection based on a filter and an adaptive distance to improve the quality of the regions of competence"

Connected through hyperedge HE-3 (induced by source paper "online local pool generation for dynamic classifier selection an extended version"):
  with members:
    - "deslib a dynamic ensemble selection library in python"
    - "meta des"
    - "should we really use post hoc tests based on mean ranks"

Connected through hyperedge HE-4 (induced by source paper "combining multiple algorithms in classifier ensembles using generalized mixture functions"):
  with members:
    - "xgboost a scalable tree boosting system"
    - "a method for image reduction based on a generalization of ordered weighted averaging functions"
    - "on meta learning for dynamic ensemble selection"

Connected through hyperedge HE-5 (induced by source paper "deslib a dynamic ensemble selection library in python"):
  with members:
    - "fire des enhanced online pruning of base classifiers for dynamic ensemble selection"

Connected through hyperedge HE-6 (induced by source paper "analyzing different prototype selection techniques for dynamic classifier and ensemble selection"):
  with members:
    - "a deep analysis of the meta des framework for dynamic selection of ensemble of classifiers"
    - "meta des"
    - "on meta learning for dynamic ensemble selection"
    - "a method for dynamic ensemble selection based on a filter and an adaptive distance to improve the quality of the regions of competence"

Connected through hyperedge HE-7 (induced by source paper "a deep analysis of the meta des framework for dynamic selection of ensemble of classifiers"):
  with members:
    - "meta des"
    - "on meta learning for dynamic ensemble selection"
    - "a method for dynamic ensemble selection based on a filter and an adaptive distance to improve the quality of the regions of competence"

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)