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: "equity of attention amortizing individual fairness in rankings"
  Abstract: Rankings of people and items are at the heart of selection-making, match-making, and recommender systems, ranging from employment sites to sharing economy platforms. As ranking positions influence the amount of attention the ranked subjects receive, biases in rankings can lead to unfair distribution of opportunities and resources such as jobs or income. This paper proposes new measures and mechanisms to quantify and mitigate unfairness from a bias inherent to all rankings, namely, the position bias which leads to disproportionately less attention being paid to low-ranked subjects. Our approach differs from recent fair ranking approaches in two important ways. First, existing works measure unfairness at the level of subject groups while our measures capture unfairness at the level of individual subjects, and as such subsume group unfairness. Second, as no single ranking can achieve individual attention fairness, we propose a novel mechanism that achieves amortized fairness, where attention accumulated across a series of rankings is proportional to accumulated relevance. We formulate the challenge of achieving amortized individual fairness subject to constraints on ranking quality as an online optimization problem and show that it can be solved as an integer linear program. Our experimental evaluation reveals that unfair attention distribution in rankings can be substantial, and demonstrates that our method can improve individual fairness while retaining high ranking quality.

Connected through hyperedge HE-1 (induced by source paper "fairsight visual analytics for fairness in decision making"):
  with members:
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "manifold a model agnostic framework for interpretation and diagnosis of machine learning models"
    - "a nutritional label for rankings"
    - "using image fairness representations in diversity based re ranking for recommendations"
    - "rulematrix visualizing and understanding classifiers with rules"
    - "fairness of exposure in rankings"
    - "fa ir a fair top k ranking algorithm"
    - "proxy non discrimination in data driven systems"

Connected through hyperedge HE-2 (induced by source paper "fairrec two sided fairness for personalized recommendations in two sided platforms"):
  with members:
    - "fairness aware ranking in search recommendation systems with application to linkedin talent search"
    - "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"
    - "almost envy free allocations with connected bundles"
    - "fair division under cardinality constraints"
    - "groupwise maximin fair allocation of indivisible goods"

Connected through hyperedge HE-3 (induced by source paper "fairness in recommendation ranking through pairwise comparisons"):
  with members:
    - "putting fairness principles into practice challenges metrics and improvements"
    - "nuanced metrics for measuring unintended bias with real data for text classification"
    - "estimating position bias without intrusive interventions"
    - "policy learning for fairness in ranking"
    - "selection problems in the presence of implicit bias"
    - "mitigating unwanted biases with adversarial learning"
    - "top k off policy correction for a reinforce recommender system"
    - "learning adversarially fair and transferable representations"

Connected through hyperedge HE-4 (induced by source paper "fair learning to rank from implicit feedback"):
  with members:
    - "fairness aware ranking in search recommendation systems with application to linkedin talent search"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "bias in bios a case study of semantic representation bias in a high stakes setting"):
  with members:
    - "algorithmic fairness"
    - "gender bias in coreference resolution"
    - "fairness under composition"
    - "word embeddings quantify 100 years of gender and ethnic stereotypes"
    - "gender bias in coreference resolution evaluation and debiasing methods"
    - "attention is all you need"
    - "racial disparity in natural language processing a case study of social media african american english"
    - "advances in pre training distributed word representations"

Connected through hyperedge HE-6 (induced by source paper "summarizing user generated textual content motivation and methods for fairness in algorithmic summaries"):
  with members:
    - "discrimination in the age of algorithms"
    - "fair and diverse dpp based data summarization"
    - "simplicity creates inequity implications for fairness stereotypes and interpretability"
    - "fair clustering through fairlets"
    - "equality of voice towards fair representation in crowdsourced top k recommendations"
    - "selection problems in the presence of implicit bias"
    - "a survey on neural network based summarization methods"
    - "text summarization techniques a brief survey"

Connected through hyperedge HE-7 (induced by source paper "fairness aware ranking in search recommendation systems with application to linkedin talent search"):
  with members:
    - "talent search and recommendation systems at linkedin practical challenges and lessons learned"
    - "fairness of exposure in rankings"
    - "ranking with fairness constraints"
    - "multiwinner voting with fairness constraints"
    - "learning non discriminatory predictors"
    - "algorithmic decision making and the cost of fairness"
    - "fa ir a fair top k ranking algorithm"
    - "semantics derived automatically from language corpora contain human like biases"

Connected through hyperedge HE-8 (induced by source paper "pairwise fairness for ranking and regression"):
  with members:
    - "fair regression quantitative definitions and reduction based algorithms"
    - "nuanced metrics for measuring unintended bias with real data for text classification"
    - "policy learning for fairness in ranking"
    - "optimization with non differentiable constraints with applications to fairness recall churn and other goals"
    - "reducing disparate exposure in ranking a learning to rank approach"
    - "fairness of exposure in rankings"
    - "two player games for efficient non convex constrained optimization"
    - "a reductions approach to fair classification"

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)