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: "fairness aware ranking in search recommendation systems with application to linkedin talent search"
  Abstract: We present a framework for quantifying and mitigating algorithmic bias in mechanisms designed for ranking individuals, typically used as part of web-scale search and recommendation systems. We first propose complementary measures to quantify bias with respect to protected attributes such as gender and age. We then present algorithms for computing fairness-aware re-ranking of results. For a given search or recommendation task, our algorithms seek to achieve a desired distribution of top ranked results with respect to one or more protected attributes. We show that such a framework can be tailored to achieve fairness criteria such as equality of opportunity and demographic parity depending on the choice of the desired distribution. We evaluate the proposed algorithms via extensive simulations over different parameter choices, and study the effect of fairness-aware ranking on both bias and utility measures. We finally present the online A/B testing results from applying our framework towards representative ranking in LinkedIn Talent Search, and discuss the lessons learned in practice. Our approach resulted in tremendous improvement in the fairness metrics (nearly three fold increase in the number of search queries with representative results) without affecting the business metrics, which paved the way for deployment to 100% of LinkedIn Recruiter users worldwide. Ours is the first large-scale deployed framework for ensuring fairness in the hiring domain, with the potential positive impact for more than 630M LinkedIn members.

Connected through hyperedge HE-1 (induced by source paper "fairrec two sided fairness for personalized recommendations in two sided platforms"):
  with members:
    - "incremental fairness in two sided market platforms on smoothly updating recommendations"
    - "estimating position bias without intrusive interventions"
    - "multi stakeholder recommendation and its connection to multi sided fairness"
    - "comparing approximate relaxations of envy freeness"
    - "equity of attention amortizing individual fairness in rankings"
    - "almost envy free allocations with connected bundles"
    - "fair division under cardinality constraints"
    - "groupwise maximin fair allocation of indivisible goods"

Connected through hyperedge HE-2 (induced by source paper "interventions for ranking in the presence of implicit bias"):
  with members:
    - "coresets for clustering with fairness constraints"
    - "toward controlling discrimination in online ad auctions"
    - "pairwise fairness for ranking and regression"
    - "fair and diverse dpp based data summarization"
    - "classification with fairness constraints a meta algorithm with provable guarantees"
    - "fairness of exposure in rankings"
    - "multiwinner voting with fairness constraints"
    - "fa ir a fair top k ranking algorithm"

Connected through hyperedge HE-3 (induced by source paper "fair learning to rank from implicit feedback"):
  with members:
    - "achieving fairness in the stochastic multi armed bandit problem"
    - "fair logistic regression an adversarial perspective"
    - "the few get richer a surprising consequence of popularity based rankings"
    - "intervention harvesting for context dependent examination bias estimation"
    - "fairness in recommendation ranking through pairwise comparisons"
    - "estimating position bias without intrusive interventions"
    - "policy learning for fairness in ranking"
    - "pairwise fairness for ranking and regression"

Connected through hyperedge HE-4 (induced by source paper "practical compositional fairness understanding fairness in multi task ml systems"):
  with members:
    - "putting fairness principles into practice challenges metrics and improvements"
    - "nuanced metrics for measuring unintended bias with real data for text classification"
    - "fairness in recommendation ranking through pairwise comparisons"
    - "the fairness of risk scores beyond classification bipartite ranking and the xauc metric"
    - "pairwise fairness for ranking and regression"
    - "training well generalizing classifiers for fairness metrics and other data dependent constraints"
    - "multiaccuracy black box post processing for fairness in classification"
    - "fairness under composition"

Connected through hyperedge HE-5 (induced by source paper "mitigating bias in algorithmic hiring evaluating claims and practices"):
  with members:
    - "what does it mean to solve the problem of discrimination in hiring social technical and legal perspectives from the uk on automated hiring systems"
    - "50 years of test un fairness lessons for machine learning"
    - "toward fairness in ai for people with disabilities a research roadmap"
    - "problem formulation and fairness"
    - "discrimination in the age of algorithms"
    - "learning adversarially fair and transferable representations"
    - "ranking with fairness constraints"
    - "algorithmic decision making and the cost of fairness"

Connected through hyperedge HE-6 (induced by source paper "what you see is what you get the impact of representation criteria on human bias in hiring"):
  with members:
    - "bias in bios a case study of semantic representation bias in a high stakes setting"
    - "discrimination in the age of algorithms"
    - "fairness and accountability design needs for algorithmic support in high stakes public sector decision making"
    - "ranking with fairness constraints"
    - "men also like shopping reducing gender bias amplification using corpus level constraints"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"

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)