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: "towards sub word level compositions for sentiment analysis of hindi english code mixed text"
  Abstract: Sentiment analysis (SA) using code-mixed data from social media has several applications in opinion mining ranging from customer satisfaction to social campaign analysis in multilingual societies. Advances in this area are impeded by the lack of a suitable annotated dataset. We introduce a Hindi-English (Hi-En) code-mixed dataset for sentiment analysis and perform empirical analysis comparing the suitability and performance of various state-of-the-art SA methods in social media. #R##N#In this paper, we introduce learning sub-word level representations in LSTM (Subword-LSTM) architecture instead of character-level or word-level representations. This linguistic prior in our architecture enables us to learn the information about sentiment value of important morphemes. This also seems to work well in highly noisy text containing misspellings as shown in our experiments which is demonstrated in morpheme-level feature maps learned by our model. Also, we hypothesize that encoding this linguistic prior in the Subword-LSTM architecture leads to the superior performance. Our system attains accuracy 4-5% greater than traditional approaches on our dataset, and also outperforms the available system for sentiment analysis in Hi-En code-mixed text by 18%.

Connected through hyperedge HE-1 (induced by source paper "unsupervised sentiment analysis for code mixed data"):
  with members:
    - "improved sentiment detection via label transfer from monolingual to synthetic code switched text"
    - "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"

Connected through hyperedge HE-2 (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"
    - "sentiment analysis of code mixed indian languages an overview of sail_code mixed shared task icon 2017"
    - "adversarial evaluation of dialogue models"
    - "grammatical constraints on intra sentential code switching from theories to working models"
    - "effective approaches to attention based neural machine translation"
    - "generating sentences from a continuous space"

Connected through hyperedge HE-3 (induced by source paper "curriculum learning strategies for hindi english codemixed sentiment analysis"):
  with members:
    - "syntactic scaffolds for semantic structures"
    - "curriculum learning by transfer learning theory and experiments with deep networks"
    - "an ensemble model for sentiment analysis of hindi english code mixed data"
    - "universal language model fine tuning for text classification"
    - "learning to generate reviews and discovering sentiment"
    - "shallow parsing pipeline for hindi english code mixed social media text"
    - "assessing the ability of lstms to learn syntax sensitive dependencies"
    - "neural machine translation of rare words with subword units"

Connected through hyperedge HE-4 (induced by source paper "deep learning for sentiment analysis a survey"):
  with members:
    - "emotional chatting machine emotional conversation generation with internal and external memory"
    - "tensor fusion network for multimodal sentiment analysis"
    - "sarcasm sign interpreting sarcasm with sentiment based monolingual machine translation"
    - "volatility prediction using financial disclosures sentiments with word embedding based ir models"
    - "deep memory networks for attitude identification"
    - "interactive attention networks for aspect level sentiment classification"
    - "recursive neural conditional random fields for aspect based sentiment analysis"
    - "a deeper look into sarcastic tweets using deep convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "sentiment analysis of code mixed languages leveraging resource rich languages"):
  with members:
    - "are emojis predictable"
    - "multilingual twitter sentiment classification the role of human annotators"

Connected through hyperedge HE-6 (induced by source paper "automatic normalization of word variations in code mixed social media text"):
  with members:
    - "text understanding 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)