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: "issues in stacked generalization"
  Abstract: Stacked generalization is a general method of using a high-level model to combine lower-level models to achieve greater predictive accuracy. In this paper we address two crucial issues which have been considered to be a 'black art' in classification tasks ever since the introduction of stacked generalization in 1992 by Wolpert: the type of generalizer that is suitable to derive the higher-level model, and the kind of attributes that should be used as its input. We find that best results are obtained when the higher-level model combines the confidence (and not just the predictions) of the lower-level ones.#R##N##R##N#We demonstrate the effectiveness of stacked generalization for combining three different types of learning algorithms for classification tasks. We also compare the performance of stacked generalization with majority vote and published results of arcing and bagging.

Connected through hyperedge HE-1 (induced by source paper "a survey of techniques all classifiers can learn from deep networks models optimizations and regularization"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "multi layered gradient boosting decision trees"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "deep ehr a survey of recent advances in deep learning techniques for electronic health record ehr analysis"
    - "sampling strategies in siamese networks for unsupervised speech representation learning"
    - "deep neural decision trees"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "embedding visual hierarchy with deep networks for large scale visual recognition"

Connected through hyperedge HE-2 (induced by source paper "techniques all classifiers can learn from deep networks models optimizations and regularization"):
  with members:
    - "deep learning for sensor based activity recognition a survey"
    - "multi layered gradient boosting decision trees"
    - "gan augmentation augmenting training data using generative adversarial networks"
    - "sampling strategies in siamese networks for unsupervised speech representation learning"
    - "deep neural decision trees"
    - "sketchygan towards diverse and realistic sketch to image synthesis"
    - "autoaugment learning augmentation policies from data"
    - "deep forest"

Connected through hyperedge HE-3 (induced by source paper "funnelling a new ensemble method for heterogeneous transfer learning and its application to cross lingual text classification"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "word translation without parallel data"
    - "cross lingual models of word embeddings an empirical comparison"
    - "distributed representations of words and phrases and their compositionality"
    - "generating sequences with recurrent neural networks"
    - "exploiting similarities among languages for machine translation"
    - "the jrc acquis a multilingual aligned parallel corpus with 20 languages"
    - "stacking classifiers for anti spam filtering of e mail"

Connected through hyperedge HE-4 (induced by source paper "svm based deep stacking networks"):
  with members:
    - "deep forest towards an alternative to deep neural networks"
    - "convexified convolutional neural networks"
    - "sparse deep stacking network for image classification"
    - "pcanet a simple deep learning baseline for image classification"
    - "deep learning using linear support vector machines"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-5 (induced by source paper "composite neural network theory and application to pm2 5 prediction"):
  with members:
    - "temporal relational ranking for stock prediction"
    - "theoretical investigation of composite neural network"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "autoencoder by forest"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

Connected through hyperedge HE-6 (induced by source paper "theoretical investigation of composite neural network"):
  with members:
    - "deep contextualized word representations"
    - "autoencoder by forest"
    - "deep residual learning for image recognition"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"
    - "imagenet large scale visual recognition challenge"
    - "popular ensemble methods an empirical study"

Connected through hyperedge HE-7 (induced by source paper "enfuzz from ensemble learning to ensemble fuzzing"):
  with members:
    - "fairfuzz a targeted mutation strategy for increasing greybox fuzz testing coverage"
    - "evaluating fuzz testing"
    - "improving function coverage with munch a hybrid fuzzing and directed symbolic execution approach"
    - "angora efficient fuzzing by principled search"
    - "slowfuzz automated domain independent detection of algorithmic complexity vulnerabilities"
    - "popular ensemble methods an empirical study"

Connected through hyperedge HE-8 (induced by source paper "distributed deep forest and its application to automatic detection of cash out fraud"):
  with members:
    - "gradient boosted feature selection"
    - "deep forest"
    - "on the robustness of decision tree learning under label noise"
    - "deep forest towards an alternative to deep neural networks"
    - "on the robustness of decision tree learning under label noise"
    - "xgboost a scalable tree boosting system"

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.AI(Artificial Intelligence)