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: "stance classification of social media users in independence movements"
  Abstract: Social media and data mining are increasingly being used to analyse political and societal issues. Characterisation of users into socio-demographic groups is crucial to improve these analyses. Here we undertake the classification of social media users as supporting or opposing ongoing independence movements in their territories. Independence movements occur in territories whose citizens have conflicting national identities; users with opposing national identities will then support or oppose the sense of being part of an independent nation that differs from the officially recognised country. We describe a methodology that relies on users' self-reported location to build datasets for three territories -- Catalonia, the Basque Country and Scotland -- and we test language-independent classifiers using four types of features. We show the effectiveness of the approach to build large annotated datasets, and the ability to achieve accurate, language-independent classification performances ranging from 85% to 97% for the three territories under study.

Connected through hyperedge HE-1 (induced by source paper "nowcasting the stance of social media users in a sudden vote the case of the greek referendum"):
  with members:
    - "political discourse on social media echo chambers gatekeepers and the price of bipartisanship"
    - "s i o no qu e penses catalonian independence and linguistic identity on social media"
    - "predicting twitter user socioeconomic attributes with network and language information"
    - "semeval 2017 task 8 rumoureval determining rumour veracity and support for rumours"
    - "i have a feeling trump will win forecasting winners and losers from user predictions on twitter"
    - "140 characters to victory using twitter to predict the uk 2015 general election"
    - "line large scale information network embedding"
    - "adam a method for stochastic optimization"

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)