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: "auditing search engines for differential satisfaction across demographics"
  Abstract: Many online services, such as search engines, social media platforms, and digital marketplaces, are advertised as being available to any user, regardless of their age, gender, or other demographic factors. However, there are growing concerns that these services may systematically underserve some groups of users. In this paper, we present a framework for internally auditing such services for differences in user satisfaction across demographic groups, using search engines as a case study. We first explain the pitfalls of naively comparing the behavioral metrics that are commonly used to evaluate search engines. We then propose three methods for measuring latent differences in user satisfaction from observed differences in evaluation metrics. To develop these methods, we drew on ideas from the causal inference literature and the multilevel modeling literature. Our framework is broadly applicable to other online services, and provides general insight into interpreting their evaluation metrics.

Connected through hyperedge HE-1 (induced by source paper "a mulching proposal"):
  with members:
    - "data science as political action grounding data science in a politics of justice"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "an intersectional definition of fairness"

Connected through hyperedge HE-2 (induced by source paper "the usage of web search for software engineering"):
  with members:
    - "interactions between health searchers and search engines"
    - "query chains learning to rank from implicit feedback"

Connected through hyperedge HE-3 (induced by source paper "equity of attention amortizing individual fairness in rankings"):
  with members:
    - "fairness of exposure in rankings"
    - "ranking with fairness constraints"
    - "fa ir a fair top k ranking algorithm"
    - "unbiased learning to rank with biased feedback"
    - "equality of opportunity in supervised learning"
    - "measuring fairness in ranked outputs"
    - "fair division via social comparison"
    - "fairness through awareness"

Connected through hyperedge HE-4 (induced by source paper "demographic differences in search engine use with implications for cohort selection"):
  with members:
    - "inferring individual attributes from search engine queries and auxiliary 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.IR(Information Retrieval)