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: "sentiment analysis of code mixed indian languages an overview of sail_code mixed shared task icon 2017"
  Abstract: Sentiment analysis is essential in many real-world applications such as stance detection, review analysis, recommendation system, and so on. Sentiment analysis becomes more difficult when the data is noisy and collected from social media. India is a multilingual country; people use more than one languages to communicate within themselves. The switching in between the languages is called code-switching or code-mixing, depending upon the type of mixing. This paper presents overview of the shared task on sentiment analysis of code-mixed data pairs of Hindi-English and Bengali-English collected from the different social media platform. The paper describes the task, dataset, evaluation, baseline and participant's systems.

Connected through hyperedge HE-1 (induced by source paper "improved sentiment detection via label transfer from monolingual to synthetic code switched text"):
  with members:
    - "a deep generative model for code switched text"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "large scale crowdsourcing and characterization of twitter abusive behavior"
    - "adversarial evaluation of dialogue models"
    - "grammatical constraints on intra sentential code switching from theories to working models"
    - "towards sub word level compositions for sentiment analysis of hindi english code mixed text"
    - "effective approaches to attention based neural machine translation"
    - "generating sentences from a continuous space"

Connected through hyperedge HE-2 (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"
    - "language identification of bengali english code mixed data using character phonetic based lstm models"
    - "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-3 (induced by source paper "girnet interleaved multi task recurrent state sequence models"):
  with members:
    - "meta multi task learning for sequence modeling"
    - "exploring shared structures and hierarchies for multiple nlp tasks"
    - "adversarial multi task learning for text classification"
    - "an overview of multi task learning in deep neural networks"
    - "aspect level sentiment classification with deep memory network"
    - "recurrent neural network for text classification with multi task learning"
    - "neural architectures for named entity recognition"
    - "effective lstms for target dependent sentiment classification"

Connected through hyperedge HE-4 (induced by source paper "strategies for language identification in code mixed low resource languages"):
  with members:
    - "language identification of bengali english code mixed data using character phonetic based lstm models"
    - "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-5 (induced by source paper "language identification in code mixed data using multichannel neural networks and context capture"):
  with members:
    - "language identification of bengali english code mixed data using character phonetic based lstm models"
    - "design challenges and misconceptions in neural sequence labeling"
    - "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)