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: "a new anew evaluation of a word list for sentiment analysis in microblogs"
  Abstract: Sentiment analysis of microblogs such as Twitter has recently gained a fair amount of attention. One of the simplest sentiment analysis approaches compares the words of a posting against a labeled word list, where each word has been scored for valence, -- a 'sentiment lexicon' or 'affective word lists'. There exist several affective word lists, e.g., ANEW (Affective Norms for English Words) developed before the advent of microblogging and sentiment analysis. I wanted to examine how well ANEW and other word lists performs for the detection of sentiment strength in microblog posts in comparison with a new word list specifically constructed for microblogs. I used manually labeled postings from Twitter scored for sentiment. Using a simple word matching I show that the new word list may perform better than ANEW, though not as good as the more elaborate approach found in SentiStrength.

Connected through hyperedge HE-1 (induced by source paper "how impersonators exploit instagram to generate fake engagement"):
  with members:
    - "netspam a network based spam detection framework for reviews in online social media"
    - "in a world that counts clustering and detecting fake social engagement at scale"
    - "the rise of social bots"
    - "mytweet via instagram exploring user behaviour across multiple social networks"
    - "on the reliability of profile matching across large online social networks"

Connected through hyperedge HE-2 (induced by source paper "natural language processing sentiment analysis and clinical analytics"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "network analysis of recurring youtube spam campaigns"

Connected through hyperedge HE-3 (induced by source paper "a dip into a deep well online political advertisements valence and european electoral campaigning"):
  with members:
    - "investigating italian disinformation spreading on twitter in the context of 2019 european elections"

Connected through hyperedge HE-4 (induced by source paper "sentiment analysis of german twitter"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "semeval 2017 task 4 sentiment analysis in twitter"
    - "from softmax to sparsemax a sparse model of attention and multi label classification"
    - "generating sentiment lexicons for german twitter"
    - "why should i trust you explaining the predictions of any classifier"
    - "simple and accurate dependency parsing using bidirectional lstm feature representations"
    - "part of speech tagging with bidirectional long short term memory recurrent neural network"
    - "in quest of significance identifying types of twitter sentiment events that predict spikes in sales"

Connected through hyperedge HE-5 (induced by source paper "danish stance classification and rumour resolution"):
  with members:
    - "fake news detection using stance classification a survey"
    - "but fit at semeval 2019 task 7 determining the rumour stance with pre trained deep bidirectional transformers"
    - "clearumor at semeval 2019 task 7 convolving elmo against rumors"
    - "a retrospective analysis of the fake news challenge stance detection task"
    - "the fact extraction and verification fever shared task"
    - "detection and resolution of rumours in social media a survey"
    - "learning word vectors for 157 languages"
    - "rumoureval 2019 determining rumour veracity and support for rumours"

Connected through hyperedge HE-6 (induced by source paper "check it a plugin for detecting and reducing the spread of fake news and misinformation on the web"):
  with members:
    - "automated fact checking task formulations methods and future directions"
    - "fakenewsnet a data repository with news content social context and dynamic information for studying fake news on social media"
    - "this just in fake news packs a lot in title uses simpler repetitive content in text body more similar to satire than real news"
    - "a stylometric inquiry into hyperpartisan and fake news"
    - "csi a hybrid deep model for fake news detection"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "deep learning and the information bottleneck principle"

Connected through hyperedge HE-7 (induced by source paper "affect in tweets using experts model"):
  with members:
    - "learning to generate reviews and discovering sentiment"
    - "seernet at semeval 2018 task 1 domain adaptation for affect in tweets"
    - "wassa 2017 shared task on emotion intensity"
    - "seernet at emoint 2017 tweet emotion intensity estimator"
    - "emotion intensities in tweets"
    - "enriching word vectors with subword information"
    - "skip thought vectors"
    - "depechemood a lexicon for emotion analysis from crowd annotated news"

Connected through hyperedge HE-8 (induced by source paper "towards successful social media advertising predicting the influence of commercial tweets"):
  with members:
    - "tweet2vec character based distributed representations for social media"
    - "tweet2vec learning tweet embeddings using character level cnn lstm encoder decoder"
    - "klout score measuring influence across multiple social networks"
    - "who will retweet this automatically identifying and engaging strangers on twitter to spread information"
    - "distributed representations of sentences and documents"
    - "can cascades be predicted"
    - "efficient estimation of word representations in vector space"
    - "twitter mood predicts the stock market"

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.IR(Information Retrieval)