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 in recommendation ranking through pairwise comparisons"
  Abstract: Recommender systems are one of the most pervasive applications of machine learning in industry, with many services using them to match users to products or information. As such it is important to ask: what are the possible fairness risks, how can we quantify them, and how should we address them? In this paper we offer a set of novel metrics for evaluating algorithmic fairness concerns in recommender systems. In particular we show how measuring fairness based on pairwise comparisons from randomized experiments provides a tractable means to reason about fairness in rankings from recommender systems. Building on this metric, we offer a new regularizer to encourage improving this metric during model training and thus improve fairness in the resulting rankings. We apply this pairwise regularization to a large-scale, production recommender system and show that we are able to significantly improve the system's pairwise fairness.

Connected through hyperedge HE-1 (induced by source paper "on the fairness of randomized trials for recommendation with heterogeneous demographics and beyond"):
  with members:
    - "average individual fairness algorithms generalization and experiments"
    - "policy learning for fairness in ranking"
    - "less is better unweighted data subsampling via influence function"
    - "delayed impact of fair machine learning"
    - "learning optimal fair policies"
    - "causal embeddings for recommendation"
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "deepfm a factorization machine based neural network for ctr prediction"

Connected through hyperedge HE-2 (induced by source paper "achieving fairness in the stochastic multi armed bandit problem"):
  with members:
    - "advancing subgroup fairness via sleeping experts"
    - "fair logistic regression an adversarial perspective"
    - "combinatorial sleeping bandits with fairness constraints"
    - "policy learning for fairness in ranking"
    - "equal opportunity in online classification with partial feedback"
    - "introduction to multi armed bandits"
    - "online learning with an unknown fairness metric"
    - "an algorithmic framework to control bias in bandit based personalization"

Connected through hyperedge HE-3 (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"
    - "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:
    - "fairness aware ranking in search recommendation systems with application to linkedin talent search"
    - "putting fairness principles into practice challenges metrics and improvements"
    - "nuanced metrics for measuring unintended bias with real data for text classification"
    - "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 "toward a better trade off between performance and fairness with kernel based distribution matching"):
  with members:
    - "transfer of machine learning fairness across domains"
    - "putting fairness principles into practice challenges metrics and improvements"
    - "nuanced metrics for measuring unintended bias with real data for text classification"
    - "the fairness of risk scores beyond classification bipartite ranking and the xauc metric"
    - "mitigating unwanted biases with adversarial learning"
    - "learning adversarially fair and transferable representations"
    - "a reductions approach to fair classification"
    - "data decisions and theoretical implications when adversarially learning fair representations"

Connected through hyperedge HE-6 (induced by source paper "addressing marketing bias in product recommendations"):
  with members:
    - "putting fairness principles into practice challenges metrics and improvements"
    - "exploring author gender in book rating and recommendation"
    - "beyond parity fairness objectives for collaborative filtering"
    - "unbiased learning to rank with biased feedback"
    - "recommendations as treatments debiasing learning and evaluation"

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)