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: "multilingual twitter sentiment classification the role of human annotators"
  Abstract: What are the limits of automated Twitter sentiment classification? We analyze a large set of manually labeled tweets in different languages, use them as training data, and construct automated classification models. It turns out that the quality of classification models depends much more on the quality and size of training data than on the type of the model trained. Experimental results indicate that there is no statistically significant difference between the performance of the top classification models. We quantify the quality of training data by applying various annotator agreement measures, and identify the weakest points of different datasets. We show that the model performance approaches the inter-annotator agreement when the size of the training set is sufficiently large. However, it is crucial to regularly monitor the self- and inter-annotator agreements since this improves the training datasets and consequently the model performance. Finally, we show that there is strong evidence that humans perceive the sentiment classes (negative, neutral, and positive) as ordered.

Connected through hyperedge HE-1 (induced by source paper "garbage in garbage out do machine learning application papers in social computing report where human labeled training data comes from"):
  with members:
    - "about ml annotation and benchmarking on understanding and transparency of machine learning lifecycles"
    - "ores lowering barriers with participatory machine learning in wikipedia"
    - "decision provenance harnessing data flow for accountable systems"
    - "towards traceability in data ecosystems using a bill of materials model"
    - "measurement and fairness"
    - "datasheets for datasets"
    - "the dataset nutrition label a framework to drive higher data quality standards"
    - "the numpy array a structure for efficient numerical computation"

Connected through hyperedge HE-2 (induced by source paper "a new statistical approach for comparing algorithms for lexicon based sentiment analysis"):
  with members:
    - "twitter sentiment analysis lexicon method machine learning method and their combination"

Connected through hyperedge HE-3 (induced by source paper "evomsa a multilingual evolutionary approach for sentiment analysis"):
  with members:
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "amobee at semeval 2018 task 1 gru neural network with a cnn attention mechanism for sentiment classification"
    - "ug18 at semeval 2018 task 1 generating additional training data for predicting emotion intensity in spanish"
    - "learning word vectors for 157 languages"
    - "eitaka at semeval 2018 task 1 an ensemble of n channels convnet and xgboost regressors for emotion analysis of tweets"
    - "seernet at semeval 2018 task 1 domain adaptation for affect in tweets"
    - "bb_twtr at semeval 2017 task 4 twitter sentiment analysis with cnns and lstms"
    - "niletmrg at semeval 2017 task 4 arabic sentiment analysis"

Connected through hyperedge HE-4 (induced by source paper "emotions are universal learning sentiment based representations of resource poor languages using siamese networks"):
  with members:
    - "tweet2vec character based distributed representations for social media"
    - "neural architectures for named entity recognition"
    - "transition based dependency parsing with stack long short term memory"
    - "empirical evaluation of gated recurrent neural networks on sequence modeling"
    - "convolutional neural networks for sentence classification"
    - "neural machine translation by jointly learning to align and translate"
    - "grammar as a foreign language"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"

Connected through hyperedge HE-5 (induced by source paper "forex trading and twitter spam bots and reputation manipulation"):
  with members:
    - "how to evaluate sentiment classifiers for twitter time ordered data"
    - "twitter sentiment around the earnings announcement events"
    - "the effects of twitter sentiment on stock price returns"
    - "i wish i didn t say that analyzing and predicting deleted messages in twitter"

Connected through hyperedge HE-6 (induced by source paper "skin tone emoji and sentiment on twitter"):
  with members:
    - "confounds and consequences in geotagged twitter data"
    - "sentiment of emojis"
    - "gender identity and lexical variation in social media"

Connected through hyperedge HE-7 (induced by source paper "how to evaluate sentiment classifiers for twitter time ordered data"):
  with members:
    - "an infra structure for performance estimation and experimental comparison of predictive models in r"
    - "nrc canada building the state of the art in sentiment analysis of tweets"

Connected through hyperedge HE-8 (induced by source paper "sentiment analysis of code mixed languages leveraging resource rich languages"):
  with members:
    - "are emojis predictable"
    - "towards sub word level compositions for sentiment analysis of hindi english code mixed text"

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)