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: "calibrated fairness in bandits"
  Abstract: We study fairness within the stochastic, multi-armed bandit (MAB) decision making framework. We adapt the fairness framework of "treating similar individuals similarly" [5] to this seing. Here, an 'individual' corresponds to an arm and two arms are 'similar' if they have a similar quality distribution. First, we adopt a smoothness constraint that if two arms have a similar quality distribution then the probability of selecting each arm should be similar. In addition, we dene the fairness regret, which corresponds to the degree to which an algorithm is not calibrated, where perfect calibration requires that the probability of selecting an arm is equal to the probability with which the arm has the best quality realization. We show that a variation on ompson sampling satises smooth fairness for total variation distance, and give an O((kT) 2/3) bound on fairness regret. is complements prior work [12], which protects an on-average beer arm from being less favored. We also explain how to extend our algorithm to the dueling bandit seing. ACM Reference format:

Connected through hyperedge HE-1 (induced by source paper "fairness in learning based sequential decision algorithms a survey"):
  with members:
    - "achieving fairness in the stochastic multi armed bandit problem"
    - "fair contextual multi armed bandits theory and experiments"
    - "on the long term impact of algorithmic decision policies effort unfairness and feature segregation through social learning"
    - "the disparate equilibria of algorithmic decision making when individuals invest rationally"
    - "a survey on bias and fairness in machine learning"
    - "fairness with dynamics"
    - "group retention when using machine learning in sequential decision making the interplay between user dynamics and fairness"
    - "combinatorial sleeping bandits with fairness constraints"

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"
    - "fairness in recommendation ranking through pairwise comparisons"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "mathematical notions vs human perception of fairness a descriptive approach to fairness for machine learning"):
  with members:
    - "improving fairness in machine learning systems what do industry practitioners need"
    - "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices"
    - "it s reducing a human being to a percentage perceptions of justice in algorithmic decisions"
    - "a moral framework for understanding of fair ml through economic models of equality of opportunity"
    - "how do fairness definitions fare examining public attitudes towards algorithmic definitions of fairness"
    - "manipulating and measuring model interpretability"
    - "fairness and accountability design needs for algorithmic support in high stakes public sector decision making"
    - "a voting based system for ethical decision making"

Connected through hyperedge HE-4 (induced by source paper "fair contextual multi armed bandits theory and experiments"):
  with members:
    - "achieving fairness in the stochastic multi armed bandit problem"
    - "reinforcement learning with fairness constraints for resource distribution in human robot teams"
    - "combinatorial sleeping bandits with fairness constraints"
    - "learning contextual bandits in a non stationary environment"
    - "robot assisted tower construction a resource distribution task to study human robot collaboration and interaction with groups of people"
    - "fairness in learning classic and contextual bandits"
    - "fair algorithms for infinite and contextual bandits"
    - "rawlsian fairness for machine learning"

Connected through hyperedge HE-5 (induced by source paper "an empirical study on learning fairness metrics for compas data with human supervision"):
  with members:
    - "operationalizing individual fairness with pairwise fair representations"
    - "metric learning for individual fairness"
    - "eliciting and enforcing subjective individual fairness"
    - "a unified approach to quantifying algorithmic unfairness measuring individual group unfairness via inequality indices"
    - "ifair learning individually fair data representations for algorithmic decision making"
    - "equality of opportunity in supervised learning"
    - "deep metric learning using triplet network"
    - "a survey on metric learning for feature vectors and structured data"

Connected through hyperedge HE-6 (induced by source paper "equal opportunity in online classification with partial feedback"):
  with members:
    - "two player games for efficient non convex constrained optimization"
    - "a reductions approach to fair classification"
    - "runaway feedback loops in predictive policing"
    - "fairness in learning classic and contextual bandits"
    - "equality of opportunity in supervised learning"
    - "inherent trade offs in the fair determination of risk scores"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "from average case complexity to improper learning complexity"

Connected through hyperedge HE-7 (induced by source paper "learning fair classifiers in online stochastic settings"):
  with members:
    - "equal opportunity in online classification with partial feedback"
    - "online learning with an unknown fairness metric"
    - "on preserving non discrimination when combining expert advice"
    - "algorithmic decision making and the cost of fairness"
    - "equality of opportunity in supervised learning"
    - "inherent trade offs in the fair determination of risk scores"

Connected through hyperedge HE-8 (induced by source paper "the frontiers of fairness in machine learning"):
  with members:
    - "delayed impact of fair machine learning"
    - "downstream effects of affirmative action"
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "a short term intervention for long term fairness in the labor market"
    - "multiaccuracy black box post processing for fairness in classification"
    - "online learning with an unknown fairness metric"
    - "selection problems in the presence of implicit bias"
    - "mitigating unwanted biases with adversarial learning"

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.LG(Machine Learning)