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: "content and network dynamics behind egyptian political polarization on twitter"
  Abstract: There is little doubt about whether social networks play a role in modern protests. This agreement has triggered an entire research avenue, in which social structure and content analysis have been central --but are typically exploited separately. #R##N#Here, we combine these two approaches to shed light on the opinion evolution dynamics in Egypt during the summer of 2013 along two axes (Islamist/Secularist, pro/anti-military intervention). We intend to find traces of opinion changes in Egypt's population, paralleling those in the international community --which oscillated from sympathetic to condemnatory as civil clashes grew. We find little evidence of people "switching" sides, along with clear changes in volume in both pro- and anti-military camps. #R##N#Our work contributes new insights into the dynamics of large protest movements, specially in the aftermath of the main events --rather unattended previously. It questions the standard narrative concerning a simplistic mapping between Secularist/pro-military and Islamist/anti-military. Finally, our conclusions provide empirical validation to sociological models regarding the behavior of individuals in conflictive contexts.

Connected through hyperedge HE-1 (induced by source paper "quantifying polarization on twitter the kavanaugh nomination"):
  with members:
    - "unsupervised user stance detection on twitter"
    - "devam vs tamam 2018 turkish elections"
    - "automatic stance detection using end to end memory networks"
    - "trump vs hillary what went viral during the 2016 us presidential election"
    - "bag of tricks for efficient text classification"
    - "failedrevolutions using twitter to study the antecedents of isis support"

Connected through hyperedge HE-2 (induced by source paper "a survey on computational politics"):
  with members:
    - "studying fake news via network analysis detection and mitigation"
    - "tweeting mps digital engagement between citizens and members of parliament in the uk"
    - "political discussions in homogeneous and cross cutting communication spaces"
    - "political discourse on social media echo chambers gatekeepers and the price of bipartisanship"
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "election bias comparing polls and twitter in the 2016 u s election"
    - "leveraging the crowd to detect and reduce the spread of fake news and misinformation"
    - "mining the demographics of political sentiment from twitter using learning from label proportions"

Connected through hyperedge HE-3 (induced by source paper "your stance is exposed analysing possible factors for stance detection on social media"):
  with members:
    - "unsupervised user stance detection on twitter"
    - "assessing sentiment of the expressed stance on social media"
    - "automatic stance detection using end to end memory networks"
    - "topical stance detection for twitter a two phase lstm model using attention"
    - "discourse aware rumour stance classification in social media using sequential classifiers"
    - "hiding individuals and communities in a social network"
    - "friends and enemies of clinton and trump using context for detecting stance in political tweets"
    - "reducing controversy by connecting opposing views"

Connected through hyperedge HE-4 (induced by source paper "hateful people or hateful bots detection and characterization of bots spreading religious hatred in arabic social media"):
  with members:
    - "universal dependency parsing from scratch"
    - "mind your pov convergence of articles and editors towards wikipedia s neutrality norm"
    - "deep learning for hate speech detection in tweets"
    - "online human bot interactions detection estimation and characterization"
    - "botornot a system to evaluate social bots"
    - "dissecting a social botnet growth content and influence in twitter"
    - "analyzing the targets of hate in online social media"
    - "the rise of social bots"

Connected through hyperedge HE-5 (induced by source paper "embedding based qualitative analysis of polarization in turkey"):
  with members:
    - "unsupervised user stance detection on twitter"
    - "devam vs tamam 2018 turkish elections"
    - "community interaction and conflict on the web"
    - "to kavanaugh or not to kavanaugh that is the polarizing question"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "semantics derived automatically from language corpora contain human like biases"
    - "enriching word vectors with subword information"
    - "bag of tricks for efficient text classification"

Connected through hyperedge HE-6 (induced by source paper "semeval 2016 task 4 sentiment analysis in twitter"):
  with members:
    - "twise at semeval 2016 task 4 twitter sentiment classification"
    - "insight 1 at semeval 2016 task 4 convolutional neural networks for sentiment classification and quantification"
    - "optimizing text quantifiers for multivariate loss functions"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-7 (induced by source paper "semeval 2017 task 4 sentiment analysis in twitter"):
  with members:
    - "bb_twtr at semeval 2017 task 4 twitter sentiment analysis with cnns and lstms"
    - "amobee at semeval 2017 task 4 deep learning system for sentiment detection on twitter"
    - "putting things in context community specific embedding projections for sentiment analysis"
    - "a machine learning approach for opinion holder extraction in arabic language"

Connected through hyperedge HE-8 (induced by source paper "tweeting mps digital engagement between citizens and members of parliament in the uk"):
  with members:
    - "mean birds detecting aggression and bullying on twitter"
    - "a long term analysis of polarization on twitter"
    - "online human bot interactions detection estimation and characterization"
    - "botornot a system to evaluate social bots"

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.SI(Social and Information Networks)