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: "is twitter a public sphere for online conflicts a cross ideological and cross hierarchical look"
  Abstract: The rise in popularity of Twitter has led to a debate on its impact on public opinions. The optimists foresee an increase in online participation and democratization due to social media's personal and interactive nature. Cyber-pessimists, on the other hand, explain how social media can lead to selective exposure and can be used as a disguise for those in power to disseminate biased information. To investigate this debate empirically, we evaluate Twitter as a public sphere using four metrics: equality, diversity, reciprocity and quality. Using these measurements, we analyze the communication patterns between individuals of different hierarchical levels and ideologies. We do this within the context of three diverse conflicts: Israel-Palestine, US Democrats-Republicans, and FC Barcelona-Real Madrid. In all cases, we collect data around a central pair of Twitter accounts representing the two main parties. Our results show in a quantitative manner that Twitter is not an ideal public sphere for democratic conversations and that hierarchical effects are part of the reason why it is not.

Connected through hyperedge HE-1 (induced by source paper "on measuring bias in online information"):
  with members:
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "the tyranny of data the bright and dark sides of data driven decision making for social good"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "algorithmic decision making and the cost of fairness"
    - "fa ir a fair top k ranking algorithm"
    - "equality of opportunity in supervised learning"
    - "measuring fairness in ranked outputs"
    - "a confidence based approach for balancing fairness and accuracy"

Connected through hyperedge HE-2 (induced by source paper "the effect of collective attention on controversial debates on social media"):
  with members:
    - "the ebb and flow of controversial debates on social media"
    - "a long term analysis of polarization on twitter"
    - "reducing controversy by connecting opposing views"
    - "social networks under stress"
    - "quantifying controversy in social media"
    - "social determinants of content selection in the age of mis information"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"
    - "dynamical classes of collective attention in twitter"

Connected through hyperedge HE-3 (induced by source paper "quantifying search bias investigating sources of bias for political searches in social media"):
  with members:
    - "events and controversies influences of a shocking news event on information seeking"
    - "discrimination in online ad delivery"
    - "topical interests and the mitigation of search engine bias"

Connected through hyperedge HE-4 (induced by source paper "quote rts on twitter usage of the new feature for political discourse"):
  with members:
    - "antisocial behavior in online discussion communities"
    - "stfu noob predicting crowdsourced decisions on toxic behavior in online games"

Connected through hyperedge HE-5 (induced by source paper "failedrevolutions using twitter to study the antecedents of isis support"):
  with members:
    - "content and network dynamics behind egyptian political polarization on twitter"
    - "online social media in the syria conflict encompassing the extremes and the in betweens"
    - "is the sample good enough comparing data from twitter s streaming api with twitter s firehose"

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)