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: "how algorithmic popularity bias hinders or promotes quality"
  Abstract: Algorithms that favor popular items are used to help us select among many choices, from top-ranked search engine results to highly-cited scientific papers. The goal of these algorithms is to identify high-quality items such as reliable news, credible information sources, and important discoveries–in short, high-quality content should rank at the top. Prior work has shown that choosing what is popular may amplify random fluctuations and lead to sub-optimal rankings. Nonetheless, it is often assumed that recommending what is popular will help high-quality content “bubble up” in practice. Here we identify the conditions in which popularity may be a viable proxy for quality content by studying a simple model of a cultural market endowed with an intrinsic notion of quality. A parameter representing the cognitive cost of exploration controls the trade-off between quality and popularity. Below and above a critical exploration cost, popularity bias is more likely to hinder quality. But we find a narrow intermediate regime of user attention where an optimal balance exists: choosing what is popular can help promote high-quality items to the top. These findings clarify the effects of algorithmic popularity bias on quality outcomes, and may inform the design of more principled mechanisms for techno-social cultural markets.

Connected through hyperedge HE-1 (induced by source paper "a survey on bias and fairness in machine learning"):
  with members:
    - "debiasing community detection the importance of lowly connected nodes"
    - "50 years of test un fairness lessons for machine learning"
    - "diversity in faces"
    - "on measuring social biases in sentence encoders"
    - "equalizing gender biases in neural machine translation with word embeddings techniques"
    - "fair regression quantitative definitions and reduction based algorithms"
    - "proportionally fair clustering"
    - "a framework for understanding unintended consequences of machine learning"

Connected through hyperedge HE-2 (induced by source paper "information pollution by social bots"):
  with members:
    - "arming the public with artificial intelligence to counter social bots"
    - "the role of bot squads in the political propaganda on twitter"
    - "identifying and analyzing cryptocurrency manipulations in social media"
    - "analyzing the digital traces of political manipulation the 2016 russian interference twitter campaign"
    - "deep neural networks for bot detection"
    - "quantifying biases in online information exposure"
    - "the spread of low credibility content by social bots"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"

Connected through hyperedge HE-3 (induced by source paper "on the inevitability of online echo chambers"):
  with members:
    - "quantifying biases in online information exposure"
    - "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"
    - "topical interests and the mitigation of search engine bias"

Connected through hyperedge HE-4 (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"
    - "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"
    - "topical interests and the mitigation of search engine bias"

Connected through hyperedge HE-5 (induced by source paper "auditing news curation systems a case study examining algorithmic and editorial logic in apple news"):
  with members:
    - "measuring the importance of user generated content to search engines"
    - "how algorithmic confounding in recommendation systems increases homogeneity and decreases utility"
    - "a data driven analysis of workers earnings on amazon mechanical turk"
    - "semantics derived automatically from language corpora contain human like biases"
    - "analytic methods for optimizing realtime crowdsourcing"

Connected through hyperedge HE-6 (induced by source paper "the spread of low credibility content by social bots"):
  with members:
    - "anatomy of an online misinformation network"
    - "detection and resolution of rumours in social media a survey"
    - "quantifying biases in online information exposure"
    - "fake news detection on social media a data mining perspective"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "early detection of promoted campaigns on social media"
    - "limited individual attention and online virality of low quality information"
    - "online human bot interactions detection estimation and characterization"

Connected through hyperedge HE-7 (induced by source paper "anatomy of an online misinformation network"):
  with members:
    - "the spread of low credibility content by social bots"
    - "disinformation and social bot operations in the run up to the 2017 french presidential election"
    - "early detection of promoted campaigns on social media"
    - "limited individual attention and online virality of low quality information"
    - "online human bot interactions detection estimation and characterization"
    - "hoaxy a platform for tracking online misinformation"
    - "botornot a system to evaluate social bots"
    - "the rise of social bots"

Connected through hyperedge HE-8 (induced by source paper "the spread of fake news by social bots"):
  with members:
    - "limited individual attention and online virality of low quality information"
    - "online human bot interactions detection estimation and characterization"
    - "hoaxy a platform for tracking online misinformation"
    - "botornot a system to evaluate social bots"
    - "the darpa twitter bot challenge"
    - "the rise of social bots"
    - "measuring online social bubbles"
    - "partisan asymmetries in online political activity"

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)