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: "quantifying the impact of user attention on fair group representation in ranked lists"
  Abstract: In this work we introduce a novel metric for verifying group fairness in ranked lists. Our approach relies on measuring the amount of attention given to members of a protected group and comparing it to that group's representation in the investigated population. It offers two major developments compared to the state of the art. First, rather than assuming a logarithmic loss in importance as a function of the rank, we allow for attention distributions that are specific to the audited service and the habits of its users. For example, we expect a user to see more items during a single viewing of a social media feed than when they inspect the list of results for a single query on a web search engine. Second, we allow non-binary protected attributes to enable investigating inherently continuous attributes (for example political alignment on the Democratic vs. Republican spectrum) as well as to facilitate measurements across aggregated set of search results, rather than separately for each result list. Finally, we showcase the metric through a simulated audit of a hiring service, an online dating service, and a search engine. We show that knowing the usage patterns of the particular service is crucial in determining the fairness of its results---depending on the attention distribution function, the same list of results can appear biased both in favor and against a protected group.

Connected through hyperedge HE-1 (induced by source paper "discrimination through optimization how facebook s ad delivery can lead to biased outcomes"):
  with members:
    - "preference informed fairness"
    - "facebook s advertising platform new attack vectors and the need for interventions"
    - "fairness under composition"
    - "quantifying search bias investigating sources of bias for political searches in social media"
    - "myadchoices bringing transparency and control to online advertising"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "automated experiments on ad privacy settings a tale of opacity choice and discrimination"
    - "xray enhancing the web s transparency with differential correlation"

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)