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: "an algorithmic framework to control bias in bandit based personalization"
  Abstract: Personalization is pervasive in the online space as it leads to higher efficiency and revenue by allowing the most relevant content to be served to each user. However, recent studies suggest that personalization methods can propagate societal or systemic biases and polarize opinions; this has led to calls for regulatory mechanisms and algorithms to combat bias and inequality. Algorithmically, bandit optimization has enjoyed great success in learning user preferences and personalizing content or feeds accordingly. We propose an algorithmic framework that allows for the possibility to control bias or discrimination in such bandit-based personalization. Our model allows for the specification of general fairness constraints on the sensitive types of the content that can be displayed to a user. The challenge, however, is to come up with a scalable and low regret algorithm for the constrained optimization problem that arises. Our main technical contribution is a provably fast and low-regret algorithm for the fairness-constrained bandit optimization problem. Our proofs crucially leverage the special structure of our problem. Experiments on synthetic and real-world data sets show that our algorithmic framework can control bias with only a minor loss to revenue.

Connected through hyperedge HE-1 (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-2 (induced by source paper "beyond personalization research directions in multistakeholder recommendation"):
  with members:
    - "using image fairness representations in diversity based re ranking for recommendations"
    - "a fairness aware hybrid recommender system"
    - "personalizing fairness aware re ranking"
    - "synthetic attribute data for evaluating consumer side fairness"
    - "exploring author gender in book rating and recommendation"
    - "price and profit awareness in recommender systems"
    - "beyond parity fairness objectives for collaborative filtering"
    - "multisided fairness for recommendation"

Connected through hyperedge HE-3 (induced by source paper "advancing subgroup fairness via sleeping experts"):
  with members:
    - "individual fairness in hindsight"
    - "multiaccuracy black box post processing for fairness in classification"
    - "online learning with an unknown fairness metric"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "the externalities of exploration and how data diversity helps exploitation"
    - "on preserving non discrimination when combining expert advice"
    - "fairness through computationally bounded awareness"
    - "a reductions approach to fair classification"

Connected through hyperedge HE-4 (induced by source paper "temporal aspects of individual fairness"):
  with members:
    - "welfare and distributional impacts of fair classification"
    - "online learning with an unknown fairness metric"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "fair algorithms for learning in allocation problems"
    - "calibrated fairness in bandits"
    - "fairness in reinforcement learning"
    - "fairness in learning classic and contextual bandits"
    - "equality of opportunity in supervised learning"

Connected through hyperedge HE-5 (induced by source paper "balanced news using constrained bandit based personalization"):
  with members:
    - "classification with fairness constraints a meta algorithm with provable guarantees"
    - "ranking with fairness constraints"
    - "multiwinner voting with fairness constraints"
    - "measuring fairness in ranked outputs"
    - "fairness through awareness"

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)