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"
  Abstract: Dynamic ensemble selection systems work by estimating the level of competence of each classifier from a pool of classifiers. Only the most competent ones are selected to classify a given test sample. This is achieved by defining a criterion to measure the level of competence of a base classifier, such as, its accuracy in local regions of the feature space around the query instance. However, using only one criterion about the behavior of a base classifier is not sufficient to accurately estimate its level of competence. In this paper, we present a novel dynamic ensemble selection framework using meta-learning. We propose five distinct sets of meta-features, each one corresponding to a different criterion to measure the level of competence of a classifier for the classification of input samples. The meta-features are extracted from the training data and used to train a meta-classifier to predict whether or not a base classifier is competent enough to classify an input instance. During the generalization phase, the meta-features are extracted from the query instance and passed down as input to the meta-classifier. The meta-classifier estimates, whether a base classifier is competent enough to be added to the ensemble. Experiments are conducted over several small sample size classification problems, i.e., problems with a high degree of uncertainty due to the lack of training data. Experimental results show that the proposed meta-learning framework greatly improves classification accuracy when compared against current state-of-the-art dynamic ensemble selection techniques. HighlightsWe propose a novel dynamic ensemble selection framework using meta-learning.We present five sets of meta-features to measure the competence of a classifier.Results demonstrate the proposed framework outperforms current techniques.

Connected through hyperedge HE-1 (induced by source paper "dynamic deep multi modal fusion for image privacy prediction"):
  with members:
    - "towards a visual privacy advisor understanding and predicting privacy risks in images"
    - "places an image database for deep scene understanding"
    - "convolutional two stream network fusion for video action recognition"
    - "is a picture worth a thousand words a deep multi modal fusion architecture for product classification in e commerce"
    - "object scene convolutional neural networks for event recognition in images"
    - "images don t lie transferring deep visual semantic features to large scale multimodal learning to rank"
    - "very deep convolutional networks for large scale image recognition"
    - "distributed representations of sentences and documents"

Connected through hyperedge HE-2 (induced by source paper "adaptive ensemble of classifiers with regularization for imbalanced data classification"):
  with members:
    - "focal loss for dense object detection"
    - "generalization properties and implicit regularization for multiple passes sgm"
    - "xgboost a scalable tree boosting system"
    - "noiseout a simple way to prune neural networks"
    - "understanding deep learning requires rethinking generalization"
    - "deep learning in neural networks"
    - "cost sensitive learning of deep feature representations from imbalanced data"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-3 (induced by source paper "fire des enhanced online pruning of base classifiers for dynamic ensemble selection"):
  with members:
    - "analyzing different prototype selection techniques for dynamic classifier and ensemble selection"
    - "imbalanced learn a python toolbox to tackle the curse of imbalanced datasets in machine learning"
    - "should we really use post hoc tests based on mean ranks"
    - "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-4 (induced by source paper "evaluating competence measures for dynamic regressor selection"):
  with members:
    - "meta des h a dynamic ensemble selection technique using meta learning and a dynamic weighting approach"
    - "cixl2 a crossover operator for evolutionary algorithms based on population features"

Connected through hyperedge HE-5 (induced by source paper "relf robust regression extended with ensemble loss function"):
  with members:
    - "on extending neural networks with loss ensembles for text classification"

Connected through hyperedge HE-6 (induced by source paper "progressive evaluation of queries over untagged data"):
  with members:
    - "an idea an ingestion framework for data enrichment in asterixdb"
    - "verdictdb universalizing approximate query processing"
    - "sampling based query re optimization"
    - "exploiting correlations for expensive predicate evaluation"

Connected through hyperedge HE-7 (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"
    - "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-8 (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 h a dynamic ensemble selection technique using meta learning and a dynamic weighting approach"
    - "should we really use post hoc tests based on mean ranks"

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)