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: "algorithms that don t see color comparing biases in lookalike and special ad audiences"
  Abstract: Today, algorithmic models are shaping important decisions in domains such as credit, employment, or criminal justice. At the same time, these algorithms have been shown to have discriminatory effects. Some organizations have tried to mitigate these effects by removing demographic features from an algorithm's inputs. If an algorithm is not provided with a feature, one might think, then its outputs should not discriminate with respect to that feature. This may not be true, however, when there are other correlated features. In this paper, we explore the limits of this approach using a unique opportunity created by a lawsuit settlement concerning discrimination on Facebook's advertising platform. Facebook agreed to modify its Lookalike Audiences tool - which creates target sets of users for ads by identifying users who share "common qualities" with users in a source audience provided by an advertiser - by removing certain demographic features as inputs to its algorithm. The modified tool, Special Ad Audiences, is intended to reduce the potential for discrimination in target audiences. We create a series of Lookalike and Special Ad audiences based on biased source audiences - i.e., source audiences that have known skew along the lines of gender, age, race, and political leanings. We show that the resulting Lookalike and Special Ad audiences both reflect these biases, despite the fact that Special Ad Audiences algorithm is not provided with the features along which our source audiences are skewed. More broadly, we provide experimental proof that removing demographic features from a real-world algorithmic system's inputs can fail to prevent biased outputs. Organizations using algorithms to mediate access to life opportunities should consider other approaches to mitigating discriminatory effects.

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)