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: "fast and accurate sentiment classification using an enhanced naive bayes model"
  Abstract: We have explored different methods of improving the accuracy of a Naive Bayes classifier for sentiment analysis. We observed that a combination of methods like effective negation handling, word n-grams and feature selection by mutual information results in a significant improvement in accuracy. This implies that a highly accurate and fast sentiment classifier can be built using a simple Naive Bayes model that has linear training and testing time complexities. We achieved an accuracy of 88.80% on the popular IMDB movie reviews dataset. The proposed method can be generalized to a number of text categorization problems for improving speed and accuracy.

Connected through hyperedge HE-1 (induced by source paper "low shot classification a comparison of classical and deep transfer machine learning approaches"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "xlnet generalized autoregressive pretraining for language understanding"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "universal language model fine tuning for text classification"
    - "automated hate speech detection and the problem of offensive language"
    - "attention is all you need"
    - "regularizing and optimizing lstm language models"

Connected through hyperedge HE-2 (induced by source paper "text compression for sentiment analysis via evolutionary algorithms"):
  with members:
    - "inferring networks of substitutable and complementary products"
    - "efficient estimation of word representations in vector space"

Connected through hyperedge HE-3 (induced by source paper "wordnet2vec corpora agnostic word vectorization method"):
  with members:
    - "text understanding from scratch"
    - "distributed representations of sentences and documents"
    - "rehabilitation of count based models for word vector representations"
    - "efficient estimation of word representations in vector space"
    - "word emdeddings through hellinger pca"
    - "distributed representations of words and phrases and their compositionality"
    - "two step cca a new spectral method for estimating vector models of words"
    - "natural language processing almost from scratch"

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)