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: "140 characters to victory using twitter to predict the uk 2015 general election"
  Abstract: The election forecasting 'industry' is a growing one, both in the volume of scholars producing forecasts and methodological diversity. In recent years a new approach has emerged that relies on social media and particularly Twitter data to predict election outcomes. While some studies have shown the method to hold a surprising degree of accuracy there has been criticism over the lack of consistency and clarity in the methods used, along with inevitable problems of population bias. In this paper we set out a 'baseline' model for using Twitter as an election forecasting tool that we then apply to the UK 2015 General Election. The paper builds on existing literature by extending the use of Twitter as a forecasting tool to the UK context and identifying its limitations, particularly with regard to its application in a multi-party environment with geographic concentration of power for minor parties.

Connected through hyperedge HE-1 (induced by source paper "measuring the diversity of facebook reactions to research"):
  with members:
    - "jointly learning to detect emotions and predict facebook reactions"
    - "shared feelings understanding facebook reactions to scholarly articles"
    - "community interaction and conflict on the web"
    - "could scientists use altmetric com scores to predict longer term citation counts"
    - "social emotion mining techniques for facebook posts reaction prediction"
    - "anatomy of scholarly information behavior patterns in the wake of academic social media platforms"
    - "scholarly use of social media and altmetrics a review of the literature"
    - "anger is more influential than joy sentiment correlation in weibo"

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 "modelling and analysing behaviours and emotions via complex user interactions"):
  with members:
    - "an analysis of introductory programming courses at uk universities"
    - "incorporating emotion and personality based analysis in user centered modelling"
    - "can i implement your algorithm a model for reproducible research software"
    - "emotion detection from text"
    - "seeing stars exploiting class relationships for sentiment categorization with respect to rating scales"
    - "thumbs up or thumbs down semantic orientation applied to unsupervised classification of reviews"
    - "improved use of continuous attributes in c4 5"

Connected through hyperedge HE-4 (induced by source paper "a framework for detecting event related sentiments of a community"):
  with members:
    - "twitter mood predicts the stock market"
    - "thumbs up or thumbs down semantic orientation applied to unsupervised classification of reviews"

Connected through hyperedge HE-5 (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"
    - "stance classification of social media users in independence movements"
    - "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"
    - "line large scale information network embedding"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-6 (induced by source paper "adaptive polling in hierarchical social networks using blackwell dominance"):
  with members:
    - "what do your friends think efficient polling methods for networks using friendship paradox"
    - "coarse graining and the blackwell order"
    - "online reputation and polling systems data incest social learning and revealed preferences"
    - "active sequential hypothesis testing"
    - "human as real time sensors of social and physical events a case study of twitter and sports games"

Connected through hyperedge HE-7 (induced by source paper "big data would not lie prediction of the 2016 taiwan election via online heterogeneous information"):
  with members:
    - "wikipedia traffic data and electoral prediction towards theoretically informed models"
    - "paying for likes understanding facebook like fraud using honeypots"
    - "twitter mood predicts the stock market"

Connected through hyperedge HE-8 (induced by source paper "personal vs know how contacts which matter more in wiki elections"):
  with members:
    - "wikipedia traffic data and electoral prediction towards theoretically informed models"
    - "predicting links in ego networks using temporal information"

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.CY(Computers and Society)