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: "using word embeddings in twitter election classification"
  Abstract: Word embeddings and convolutional neural networks (CNN) have attracted extensive attention in various classification tasks for Twitter, e.g. sentiment classification. However, the effect of the configuration used to train and generate the word embeddings on the classification performance has not been studied in the existing literature. In this paper, using a Twitter election classification task that aims to detect election-related tweets, we investigate the impact of the background dataset used to train the embedding models, the context window size and the dimensionality of word embeddings on the classification performance. By comparing the classification results of two word embedding models, which are trained using different background corpora (e.g. Wikipedia articles and Twitter microposts), we show that the background data type should align with the Twitter classification dataset to achieve a better performance. Moreover, by evaluating the results of word embeddings models trained using various context window sizes and dimensionalities, we found that large context window and dimension sizes are preferable to improve the performance. Our experimental results also show that using word embeddings and CNN leads to statistically significant improvements over various baselines such as random, SVM with TF-IDF and SVM with word embeddings.

Connected through hyperedge HE-1 (induced by source paper "vocabulary based method for quantifying controversy in social media"):
  with members:
    - "quantifying polarization on twitter the kavanaugh nomination"
    - "community interaction and conflict on the web"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "joint non negative matrix factorization for learning ideological leaning on twitter"
    - "a distance measure for the analysis of polar opinion dynamics in social networks"
    - "characterizing information diets of social media users"
    - "reducing controversy by connecting opposing views"
    - "characterizing the language of online communities and its relation to community reception"

Connected through hyperedge HE-2 (induced by source paper "measuring relative opinion from location based social media a case study of the 2016 u s presidential election"):
  with members:
    - "validation of twitter opinion trends with national polling aggregates hillary clinton vs donald trump"
    - "election bias comparing polls and twitter in the 2016 u s election"
    - "scaling laws in geo located twitter data"
    - "distributed representations of sentences and documents"
    - "deep learning of representations looking forward"
    - "distributed representations of words and phrases and their compositionality"
    - "a meta analysis of state of the art electoral prediction from twitter data"
    - "natural language processing almost from scratch"

Connected through hyperedge HE-3 (induced by source paper "on the robustness of unsupervised and semi supervised cross lingual word embedding learning"):
  with members:
    - "a survey of cross lingual word embedding models"
    - "learning cross lingual word embeddings from twitter via distant supervision"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "why is unsupervised alignment of english embeddings from different algorithms so hard"
    - "the limitations of cross language word embeddings evaluation"
    - "xnli evaluating cross lingual sentence representations"
    - "unsupervised multilingual word embeddings"
    - "on difficulties of cross lingual transfer with order differences a case study on dependency parsing"

Connected through hyperedge HE-4 (induced by source paper "towards robust word embeddings for noisy texts"):
  with members:
    - "joint aspect and polarity classification for aspect based sentiment analysis with end to end neural networks"
    - "deep contextualized word representations"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "towards twitter sentiment classification by multi level sentiment enriched word embeddings"
    - "learning general purpose distributed sentence representations via large scale multi task learning"
    - "senteval an evaluation toolkit for universal sentence representations"
    - "improving text normalization by optimizing nearest neighbor matching"
    - "supervised learning of universal sentence representations from natural language inference data"

Connected through hyperedge HE-5 (induced by source paper "learning cross lingual word embeddings from twitter via distant supervision"):
  with members:
    - "semeval 2016 task 4 sentiment analysis in twitter"
    - "a survey of cross lingual word embedding models"
    - "how to properly evaluate cross lingual word embeddings on strong baselines comparative analyses and some misconceptions"
    - "meemi finding the middle ground in cross lingual word embeddings"
    - "bilingual sentiment embeddings joint projection of sentiment across languages"
    - "unsupervised multilingual word embeddings"
    - "on the limitations of unsupervised bilingual dictionary induction"
    - "a robust self learning method for fully unsupervised cross lingual mappings of word embeddings"

Connected through hyperedge HE-6 (induced by source paper "classifying relevant social media posts during disasters using ensemble of domain agnostic and domain specific word embeddings"):
  with members:
    - "relevancy classification of multimodal social media streams for emergency services"
    - "crisismmd multimodal twitter datasets from natural disasters"
    - "frustratingly easy meta embedding computing meta embeddings by averaging source word embeddings"
    - "enriching word vectors with subword information"
    - "twitter as a lifeline human annotated twitter corpora for nlp of crisis related messages"
    - "processing social media messages in mass emergency a survey"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-7 (induced by source paper "how polarized have we become a multimodal classification of trump followers and clinton followers"):
  with members:
    - "a selfie is worth a thousand words mining personal patterns behind user selfie posting behaviours"
    - "catching fire via likes inferring topic preferences of trump followers on twitter"
    - "do they all look the same deciphering chinese japanese and koreans by fine grained deep learning"
    - "gender politics in the 2016 u s presidential election a computer vision approach"
    - "actionable and political text classification using word embeddings and lstm"
    - "pricing the woman card gender politics between hillary clinton and donald trump"
    - "voting with feet who are leaving hillary clinton and donald trump"
    - "deciphering the 2016 u s presidential campaign in the twitter sphere a comparison of the trumpists and clintonists"

Connected through hyperedge HE-8 (induced by source paper "easy over hard a case study on deep learning"):
  with members:
    - "what is wrong with topic modeling and how to fix it using search based software engineering"
    - "efficient hyperparameter optimization and infinitely many armed bandits"
    - "hyperband a novel bandit based approach to hyperparameter optimization"
    - "a deep learning model for estimating story points"
    - "deep api learning"
    - "why is differential evolution better than grid search for tuning defect predictors"
    - "tuning for software analytics"
    - "swim synthesizing what i mean code search and idiomatic snippet synthesis"

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)