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: "topical interests and the mitigation of search engine bias"
  Abstract: Search engines have become key media for our scientific, economic, and social activities by enabling people to access information on the web despite its size and complexity. On the down side, search engines bias the traffic of users according to their page ranking strategies, and it has been argued that they create a vicious cycle that amplifies the dominance of established and already popular sites. This bias could lead to a dangerous monopoly of information. We show that, contrary to intuition, empirical data do not support this conclusion; popular sites receive far less traffic than predicted. We discuss a model that accurately predicts traffic data patterns by taking into consideration the topical interests of users and their searching behavior in addition to the way search engines rank pages. The heterogeneity of user interests explains the observed mitigation of search engines’ popularity bias.

Connected through hyperedge HE-1 (induced by source paper "on the inevitability of online echo chambers"):
  with members:
    - "quantifying biases in online information exposure"
    - "how algorithmic popularity bias hinders or promotes quality"
    - "limited individual attention and online virality of low quality information"
    - "explaining the emergence of echo chambers on social media the role of ideology and extremism"
    - "measuring online social bubbles"
    - "the role of information diffusion in the evolution of social networks"
    - "partisan asymmetries in online political activity"
    - "happiness is assortative in online social networks"

Connected through hyperedge HE-2 (induced by source paper "the few get richer a surprising consequence of popularity based rankings"):
  with members:
    - "equity of attention amortizing individual fairness in rankings"
    - "how algorithmic popularity bias hinders or promotes quality"
    - "leveraging the crowd to detect and reduce the spread of fake news and misinformation"
    - "opinion dynamics via search engines and other algorithmic gatekeepers"
    - "anyone can become a troll causes of trolling behavior in online discussions"
    - "fa ir a fair top k ranking algorithm"
    - "biased assimilation homophily and the dynamics of polarization"

Connected through hyperedge HE-3 (induced by source paper "an investigation of biases in web search engine query suggestions"):
  with members:
    - "on measuring bias in online information"
    - "efficient estimation of word representations in vector space"
    - "a novel combined term suggestion service for domain specific digital libraries"

Connected through hyperedge HE-4 (induced by source paper "quantifying biases in online information exposure"):
  with members:
    - "rating effects on social news posts and comments"
    - "hoaxy a platform for tracking online misinformation"
    - "the rise of social bots"
    - "measuring online social bubbles"
    - "data portraits connecting people of opposing views"

Connected through hyperedge HE-5 (induced by source paper "how algorithmic popularity bias hinders or promotes quality"):
  with members:
    - "quantifying biases in online information exposure"
    - "limited individual attention and online virality of low quality information"
    - "style in the age of instagram predicting success within the fashion industry using social media"
    - "the rise of social bots"
    - "virality prediction and community structure in social networks"

Connected through hyperedge HE-6 (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-7 (induced by source paper "quantifying search bias investigating sources of bias for political searches in social media"):
  with members:
    - "is twitter a public sphere for online conflicts a cross ideological and cross hierarchical look"
    - "events and controversies influences of a shocking news event on information seeking"
    - "discrimination in online ad delivery"

Connected through hyperedge HE-8 (induced by source paper "opinion dynamics via search engines and other algorithmic gatekeepers"):
  with members:
    - "hoaxy a platform for tracking online misinformation"
    - "science vs conspiracy collective narratives in the age of misinformation"
    - "collective attention in the age of mis 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)