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: "on meta learning for dynamic ensemble selection"
  Abstract: In this paper, we propose a novel dynamic ensemble selection framework using meta-learning. The framework is divided into three steps. In the first step, the pool of classifiers is generated from the training data. The second phase is responsible to extract the meta-features and train the meta-classifier. Five distinct sets of meta-features are proposed, each one corresponding to a different criterion to measure the level of competence of a classifier for the classification of a given query sample. 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. Three different training scenarios for the training of the meta-classifier are considered: problem-dependent, problem-independent and hybrid. Experimental results show that the problem-dependent scenario provides the best result. In addition, the performance of the problem-dependent scenario is strongly correlated with the recognition rate of the system. A comparison with state-of-the-art techniques shows that the proposed-dependent approach outperforms current dynamic ensemble selection techniques.

Connected through hyperedge HE-1 (induced by source paper "the mbpep a deep ensemble pruning algorithm providing high quality uncertainty prediction"):
  with members:
    - "a hardware friendly unsupervised memristive neural network with weight sharing mechanism"
    - "sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network"
    - "adam a method for stochastic optimization"
    - "adadelta an adaptive learning rate method"

Connected through hyperedge HE-2 (induced by source paper "meta des oracle meta learning and feature selection for ensemble selection"):
  with members:
    - "meta des h a dynamic ensemble selection technique using meta learning and a dynamic weighting approach"
    - "a deep analysis of the meta des framework for dynamic selection of ensemble of classifiers"
    - "meta des"
    - "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 "combining multiple algorithms in classifier ensembles using generalized mixture functions"):
  with members:
    - "meta des h a dynamic ensemble selection technique using meta learning and a dynamic weighting approach"
    - "xgboost a scalable tree boosting system"
    - "a method for image reduction based on a generalization of ordered weighted averaging functions"

Connected through hyperedge HE-4 (induced by source paper "meta des h a dynamic ensemble selection technique using meta learning and a dynamic weighting approach"):
  with members:
    - "meta des"
    - "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-5 (induced by source paper "analyzing different prototype selection techniques for dynamic classifier and ensemble selection"):
  with members:
    - "meta des h a dynamic ensemble selection technique using meta learning and a dynamic weighting approach"
    - "a deep analysis of the meta des framework for dynamic selection of ensemble of classifiers"
    - "meta des"
    - "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-6 (induced by source paper "a deep analysis of the meta des framework for dynamic selection of ensemble of classifiers"):
  with members:
    - "meta des h a dynamic ensemble selection technique using meta learning and a dynamic weighting approach"
    - "meta des"
    - "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)