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: "an empirical study on sentiment classification of chinese review using word embedding"
  Abstract: In this article, how word embeddings can be used as features in Chinese sentiment classification is presented. Firstly, a Chinese opinion corpus is built with a million comments from hotel review websites. Then the word embeddings which represent each comment are used as input in different machine learning methods for sentiment classification, including SVM, Logistic Regression, Convolutional Neural Network (CNN) and ensemble methods. These methods get better performance compared with N-gram models using Naive Bayes (NB) and Maximum Entropy (ME). Finally, a combination of machine learning methods is proposed which presents an outstanding performance in precision, recall and F1 score. After selecting the most useful methods to construct the combinational model and testing over the corpus, the final F1 score is 0.920.

Connected through hyperedge HE-1 (induced by source paper "single training dimension selection for word embedding with pca"):
  with members:
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "on the dimensionality of word embedding"
    - "nationality classification using name embeddings"
    - "simple and effective dimensionality reduction for word embeddings"
    - "all but the top simple and effective postprocessing for word representations"
    - "compressing word embeddings via deep compositional code learning"
    - "how polarized have we become a multimodal classification of trump followers and clinton followers"
    - "poincar e embeddings for learning hierarchical representations"

Connected through hyperedge HE-2 (induced by source paper "how polarized have we become a multimodal classification of trump followers and clinton followers"):
  with members:
    - "a selfie is worth a thousand words mining personal patterns behind user selfie posting behaviours"
    - "catching fire via likes inferring topic preferences of trump followers on twitter"
    - "using word embeddings in twitter election classification"
    - "do they all look the same deciphering chinese japanese and koreans by fine grained deep learning"
    - "gender politics in the 2016 u s presidential election a computer vision approach"
    - "actionable and political text classification using word embeddings and lstm"
    - "pricing the woman card gender politics between hillary clinton and donald trump"
    - "voting with feet who are leaving hillary clinton and donald trump"

Connected through hyperedge HE-3 (induced by source paper "cross lingual distillation for text classification"):
  with members:
    - "sequence level knowledge distillation"
    - "dependency sensitive convolutional neural networks for modeling sentences and documents"
    - "adversarial deep averaging networks for cross lingual sentiment classification"
    - "distilling word embeddings an encoding approach"
    - "semi supervised sequence learning"
    - "cross modal distillation for supervision transfer"
    - "fitnets hints for thin deep nets"
    - "effective use of word order for text categorization with convolutional neural networks"

Connected through hyperedge HE-4 (induced by source paper "cross lingual sentiment analysis without good translation"):
  with members:
    - "learning crosslingual word embeddings without bilingual corpora"
    - "adversarial deep averaging networks for cross lingual sentiment classification"
    - "an autoencoder approach to learning bilingual word representations"
    - "distributed representations of words and phrases and their compositionality"
    - "exploiting similarities among languages for machine translation"
    - "multilingual distributed representations without word alignment"

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.CL(Computation and Language)