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: "biases in the facebook news feed a case study on the italian elections"
  Abstract: Facebook News Feed personalization algorithm has a significant impact, on a daily basis, on the lifestyle, mood and opinion of millions of Internet users. Nonetheless, the behavior of such algorithms usually lacks transparency, motivating measurements, modeling and analysis in order to understand and improve its properties. In this paper, we propose a reproducible methodology encompassing measurements and an analytical model to capture the visibility of publishers over a News Feed. First, measurements are used to parameterize and to validate the expressive power of the proposed model. Then, we conduct a what-if analysis to assess the visibility bias incurred by the users against a baseline derived from the model. Our results indicate that a significant bias exists and it is more prominent at the top position of the News Feed. In addition, we found that the bias is non-negligible even for users that are deliberately set as neutral with respect to their political views.

Connected through hyperedge HE-1 (induced by source paper "how often should i access my online social networks"):
  with members:
    - "age of information vs value of information scheduling for cellular networked control systems"
    - "performance analysis of online social platforms"
    - "minimizing the age of information in wireless networks with stochastic arrivals"
    - "fairness in online social network timelines measurements models and mechanism design"
    - "age optimal sampling and transmission scheduling in multi source systems"
    - "optimal status update for age of information minimization with an energy harvesting source"
    - "characterizing information diets of social media users"
    - "the age of information real time status updating by multiple sources"

Connected through hyperedge HE-2 (induced by source paper "fairness in online social network timelines measurements models and mechanism design"):
  with members:
    - "do diffusion protocols govern cascade growth"
    - "closing the ai knowledge gap"
    - "fairness of exposure in rankings"
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "alpha fair coded caching"
    - "on fairness and calibration"
    - "interventions over predictions reframing the ethical debate for actuarial risk assessment"
    - "ranking with fairness constraints"

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)