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: "analyzing roles of classifiers and code mixed factors for sentiment identification"
  Abstract: Multilingual speakers often switch between languages to express themselves on social communication platforms. Sometimes, the original script of the language is preserved, while using a common script for all the languages is quite popular as well due to convenience. On such occasions, multiple languages are being mixed with different rules of grammar, using the same script which makes it a challenging task for natural language processing even in case of accurate sentiment identification. In this paper, we report results of various experiments carried out on movie reviews dataset having this code-mixing property of two languages like English and Bengali, both typed in Roman script. We have tested various machine learning algorithms trained only on English features on our code-mixed data and have achieved a maximum accuracy of 59.00% using a Na\"ive Bayes (NB) model. We have also tested various models trained on code-mixed data, as well as English features and the highest accuracy of 72.50% was obtained using a Support Vector Machine (SVM) model. Finally, we have analyzed the misclassified snippets and have discussed the challenges needed to be resolved for better accuracy.

Connected through hyperedge HE-1 (induced by source paper "code mixed to monolingual translation framework"):
  with members:
    - "normalization of transliterated words in code mixed data using seq2seq model levenshtein distance"
    - "language identification of bengali english code mixed data using character phonetic based lstm models"
    - "sentiment analysis of code mixed indian languages an overview of sail_code mixed shared task icon 2017"
    - "smt vs nmt a comparison over hindi bengali simple sentences"
    - "sentiment identification in code mixed social media text"
    - "attention is all you need"
    - "fully character level neural machine translation without explicit segmentation"
    - "a character level decoder without explicit segmentation for neural machine translation"

Connected through hyperedge HE-2 (induced by source paper "normalization of transliterated words in code mixed data using seq2seq model levenshtein distance"):
  with members:
    - "language identification of bengali english code mixed data using character phonetic based lstm models"
    - "automatic normalization of word variations in code mixed social media text"
    - "preparing bengali english code mixed corpus for sentiment analysis of indian languages"
    - "attention is all you need"
    - "deepnorm a deep learning approach to text normalization"
    - "shallow parsing pipeline for hindi english code mixed social media text"
    - "fully character level neural machine translation without explicit segmentation"
    - "rnn approaches to text normalization a challenge"

Connected through hyperedge HE-3 (induced by source paper "preparing bengali english code mixed corpus for sentiment analysis of indian languages"):
  with members:
    - "sentiment identification in code mixed social media text"
    - "complexity metric for code mixed social media text"

Connected through hyperedge HE-4 (induced by source paper "language identification of bengali english code mixed data using character phonetic based lstm models"):
  with members:
    - "fully character level neural machine translation without explicit segmentation"

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)