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: "language identification of bengali english code mixed data using character phonetic based lstm models"
  Abstract: Language identification of social media text still remains a challenging task due to properties like code-mixing and inconsistent phonetic transliterations. In this paper, we present a supervised learning approach for language identification at the word level of low resource Bengali-English code-mixed data taken from social media. We employ two methods of word encoding, namely character based and root phone based to train our deep LSTM models. Utilizing these two models we created two ensemble models using stacking and threshold technique which gave 91.78% and 92.35% accuracies respectively on our testing data.

Connected through hyperedge HE-1 (induced by source paper "code mixed to monolingual translation framework"):
  with members:
    - "analyzing roles of classifiers and code mixed factors for sentiment identification"
    - "normalization of transliterated words in code mixed data using seq2seq model levenshtein distance"
    - "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 "strategies for language identification in code mixed low resource languages"):
  with members:
    - "sentiment analysis of code mixed indian languages an overview of sail_code mixed shared task icon 2017"
    - "preparing bengali english code mixed corpus for sentiment analysis of indian languages"
    - "data augmentation for low resource neural machine translation"
    - "l2 regularization versus batch and weight normalization"
    - "one shot learning with memory augmented neural networks"
    - "matching networks for one shot learning"
    - "how transferable are neural networks in nlp applications"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-3 (induced by source paper "normalization of transliterated words in code mixed data using seq2seq model levenshtein distance"):
  with members:
    - "analyzing roles of classifiers and code mixed factors for sentiment identification"
    - "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-4 (induced by source paper "language identification in code mixed data using multichannel neural networks and context capture"):
  with members:
    - "design challenges and misconceptions in neural sequence labeling"
    - "sentiment analysis of code mixed indian languages an overview of sail_code mixed shared task icon 2017"
    - "ncrf an open source neural sequence labeling toolkit"
    - "preparing bengali english code mixed corpus for sentiment analysis of indian languages"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "bidirectional lstm crf models for sequence tagging"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"

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)