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: "improved sentiment detection via label transfer from monolingual to synthetic code switched text"
  Abstract: Multilingual writers and speakers often alternate between two languages in a single discourse, a practice called "code-switching". Existing sentiment detection methods are usually trained on sentiment-labeled monolingual text. Manually labeled code-switched text, especially involving minority languages, is extremely rare. Consequently, the best monolingual methods perform relatively poorly on code-switched text. We present an effective technique for synthesizing labeled code-switched text from labeled monolingual text, which is more readily available. The idea is to replace carefully selected subtrees of constituency parses of sentences in the resource-rich language with suitable token spans selected from automatic translations to the resource-poor language. By augmenting scarce human-labeled code-switched text with plentiful synthetic code-switched text, we achieve significant improvements in sentiment labeling accuracy (1.5%, 5.11%, 7.20%) for three different language pairs (English-Hindi, English-Spanish and English-Bengali). We also get significant gains for hate speech detection: 4% improvement using only synthetic text and 6% if augmented with real text.

Connected through hyperedge HE-1 (induced by source paper "unsupervised sentiment analysis for code mixed data"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "sentencepiece a simple and language independent subword tokenizer and detokenizer for neural text processing"
    - "massively multilingual sentence embeddings for zero shot cross lingual transfer and beyond"
    - "learning word vectors for 157 languages"
    - "sentiment analysis of code mixed languages leveraging resource rich languages"
    - "word translation without parallel data"
    - "enriching word vectors with subword information"

Connected through hyperedge HE-2 (induced by source paper "a deep generative model for code switched text"):
  with members:
    - "code switched language models using dual rnns and same source pretraining"
    - "code switching language modeling using syntax aware multi task learning"
    - "adversarial evaluation of dialogue models"
    - "generating sentences from a continuous space"
    - "character aware neural language models"
    - "a large annotated corpus for learning natural language inference"
    - "attention based models for speech recognition"
    - "long term recurrent convolutional networks for visual recognition and description"

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)