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: "devam vs tamam 2018 turkish elections"
  Abstract: On June 24, 2018, Turkey held a historical election, transforming its parliamentary system to a presidential one. One of the main questions for Turkish voters was whether to start this new political era with reelecting its long-time political leader Recep Tayyip Erdogan or not. In this paper, we analyzed 108M tweets posted in the two months leading to the election to understand the groups that supported or opposed Erdogan's reelection. We examined the most distinguishing hashtags and retweeted accounts for both groups. Our findings indicate strong polarization between both groups as they differ in terms of ideology, news sources they follow, and preferred TV entertainment.

Connected through hyperedge HE-1 (induced by source paper "quantifying polarization on twitter the kavanaugh nomination"):
  with members:
    - "unsupervised user stance detection on twitter"
    - "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"
    - "content and network dynamics behind egyptian political polarization on twitter"

Connected through hyperedge HE-2 (induced by source paper "embedding based qualitative analysis of polarization in turkey"):
  with members:
    - "unsupervised user stance detection on twitter"
    - "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"
    - "failedrevolutions using twitter to study the antecedents of isis support"

Connected through hyperedge HE-3 (induced by source paper "unsupervised user stance detection on twitter"):
  with members:
    - "failedrevolutions using twitter to study the antecedents of isis support"
    - "content and network dynamics behind egyptian political polarization on twitter"

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)