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: "mining public opinion about economic issues twitter and the u s presidential election"
  Abstract: Opinion polls have been the bridge between public opinion and politicians in elections. However, developing surveys to disclose people's feedback with respect to economic issues is limited, expensive, and time-consuming. In recent years, social media such as Twitter has enabled people to share their opinions regarding elections. Social media has provided a platform for collecting a large amount of social media data. This article proposes a computational public opinion mining approach to explore the discussion of economic issues in social media during an election. Current related studies use text mining methods independently for election analysis and election prediction; this research combines two text mining methods: sentiment analysis and topic modeling. The proposed approach has effectively been deployed on millions of tweets to analyze economic concerns of people during the 2012 US presidential election.

Connected through hyperedge HE-1 (induced by source paper "twitter speaks a case of national disaster situational awareness"):
  with members:
    - "characterizing transgender health issues in twitter"
    - "social media analysis for organizations us northeastern public and state libraries case study"
    - "characterizing diseases and disorders in gay users tweets"
    - "life never matters in the democrats mind examining strategies of retweeted social bots during a mass shooting event"
    - "what do the us west coast public libraries post on twitter"
    - "applications of deep learning and reinforcement learning to biological data"
    - "deep learning for iot big data and streaming analytics a survey"
    - "computational content analysis of negative tweets for obesity diet diabetes and exercise"

Connected through hyperedge HE-2 (induced by source paper "characterizing transgender health issues in twitter"):
  with members:
    - "characterizing diseases and disorders in gay users tweets"
    - "computational content analysis of negative tweets for obesity diet diabetes and exercise"
    - "you tweet what you eat studying food consumption through twitter"

Connected through hyperedge HE-3 (induced by source paper "what do the us west coast public libraries post on twitter"):
  with members:
    - "characterizing diseases and disorders in gay users tweets"
    - "computational content analysis of negative tweets for obesity diet diabetes and exercise"
    - "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.SI(Social and Information Networks)