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: "kaggle lshtc4 winning solution"
  Abstract: Our winning submission to the 2014 Kaggle competition for Large Scale Hier-archical Text Classi cation (LSHTC) consists mostly of an ensemble of sparsegenerative models extending Multinomial Naive Bayes. The base-classi ers con-sist of hierarchically smoothed models combining document, label, and hierar-chy level Multinomials, with feature pre-processing using variants of TF-IDFand BM25. Additional diversi cation is introduced by di erent types of foldsand random search optimization for di erent measures. The ensemble algorithmoptimizes macroFscore by predicting the documents for each label, instead ofthe usual prediction of labels per document. Scores for documents are predictedby weighted voting of base-classi er outputs with a variant of Feature-WeightedLinear Stacking. The number of documents per label is chosen using label priorsand thresholding of vote scores.This document describes the models and software used to build our solution.Reproducing the results for our solution can be done by running the scripts in-cluded in the Kaggle package

Connected through hyperedge HE-1 (induced by source paper "an analysis of hierarchical text classification using word embeddings"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "lstm a search space odyssey"
    - "an empirical study on large scale text classification with skip gram embeddings"
    - "bag of tricks for efficient text classification"
    - "character level convolutional networks for text classification"
    - "lshtc a benchmark for large scale text classification"
    - "deep learning in neural networks"
    - "evaluation measures for hierarchical classification a unified view and novel approaches"

Connected through hyperedge HE-2 (induced by source paper "classifier chains a review and perspectives"):
  with members:
    - "probabilistic regressor chains with monte carlo methods"
    - "scikit multiflow a multi output streaming framework"
    - "a scikit based python environment for performing multi label classification"
    - "multi label methods for prediction with sequential data"
    - "deep residual learning for image recognition"
    - "search based structured prediction"

Connected through hyperedge HE-3 (induced by source paper "discovering topics with neural topic models built from plsa assumptions"):
  with members:
    - "texttovec deep contextualized neural autoregressive topic models of language with distributed compositional prior"
    - "mixing dirichlet topic models and word embeddings to make lda2vec"
    - "continuous time dynamic topic models"
    - "multilingual topic models for unaligned text"
    - "natural language processing almost from scratch"
    - "machine learning in automated text categorization"

Connected through hyperedge HE-4 (induced by source paper "k nearest oracles borderline dynamic classifier ensemble selection"):
  with members:
    - "meta des"
    - "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.AI(Artificial Intelligence)