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 incentives for myopic agents"
  Abstract: We consider settings in which we wish to incentivize myopic agents (such as Airbnb landlords, who may emphasize short-term profits and property safety) to treat arriving clients fairly, in order to prevent overall discrimination against individuals or groups. We model such settings in both classical and contextual bandit models in which the myopic agents maximize rewards according to current empirical averages, but are also amenable to exogenous payments that may cause them to alter their choices. Our notion of fairness asks that more qualified individuals are never (probabilistically) preferred over less qualified ones [Joseph et al]. #R##N#We investigate whether it is possible to design inexpensive {subsidy} or payment schemes for a principal to motivate myopic agents to play fairly in all or almost all rounds. When the principal has full information about the state of the myopic agents, we show it is possible to induce fair play on every round with a subsidy scheme of total cost $o(T)$ (for the classic setting with $k$ arms, $\tilde{O}(\sqrt{k^3T})$, and for the $d$-dimensional linear contextual setting $\tilde{O}(d\sqrt{k^3 T})$). If the principal has much more limited information (as might often be the case for an external regulator or watchdog), and only observes the number of rounds in which members from each of the $k$ groups were selected, but not the empirical estimates maintained by the myopic agent, the design of such a scheme becomes more complex. We show both positive and negative results in the classic and linear bandit settings by upper and lower bounding the cost of fair subsidy schemes.

Connected through hyperedge HE-1 (induced by source paper "bayesian exploration with heterogeneous agents"):
  with members:
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "the externalities of exploration and how data diversity helps exploitation"
    - "overabundant information and learning traps"
    - "incentivizing exploration with unbiased histories"
    - "competing bandits learning under competition"
    - "optimal and myopic information acquisition"
    - "selling to a no regret buyer"
    - "descending price optimally coordinates search"

Connected through hyperedge HE-2 (induced by source paper "algorithmic and economic perspectives on fairness"):
  with members:
    - "fair classification and social welfare"
    - "the measure and mismeasure of fairness a critical review of fair machine learning"
    - "mechanism design for social good"
    - "algorithmic decision making and the cost of fairness"
    - "runaway feedback loops in predictive policing"
    - "man is to computer programmer as woman is to homemaker debiasing word embeddings"
    - "censoring representations with an adversary"
    - "discrimination in online ad delivery"

Connected through hyperedge HE-3 (induced by source paper "policy certificates towards accountable reinforcement learning"):
  with members:
    - "tighter problem dependent regret bounds in reinforcement learning without domain knowledge using value function bounds"
    - "the externalities of exploration and how data diversity helps exploitation"
    - "on polynomial time pac reinforcement learning with rich observations"
    - "is q learning provably efficient"
    - "multi step off policy learning without importance sampling ratios"
    - "unifying pac and regret uniform pac bounds for episodic reinforcement learning"
    - "fairness in reinforcement learning"
    - "fairness in learning classic and contextual bandits"

Connected through hyperedge HE-4 (induced by source paper "access to population level signaling as a source of inequality"):
  with members:
    - "preventing fairness gerrymandering auditing and learning for subgroup fairness"
    - "fairness at equilibrium in the labor market"
    - "algorithmic persuasion with no externalities"
    - "fairness in learning classic and contextual bandits"
    - "equality of opportunity in supervised learning"
    - "on the im possibility of fairness"
    - "inherent trade offs in the fair determination of risk scores"
    - "algorithmic bayesian persuasion"

Connected through hyperedge HE-5 (induced by source paper "the price of fair pca one extra dimension"):
  with members:
    - "convex formulations for fair principal component analysis"
    - "datasheets for datasets"
    - "mitigating unwanted biases with adversarial learning"
    - "learning adversarially fair and transferable representations"
    - "afif4 deep gender classification based on adaboost based fusion of isolated facial features and foggy faces"
    - "runaway feedback loops in predictive policing"
    - "fairness in learning classic and contextual bandits"
    - "equality of opportunity in supervised learning"

Connected through hyperedge HE-6 (induced by source paper "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"):
  with members:
    - "calibrated fairness in bandits"
    - "fairness in reinforcement learning"
    - "fairness in learning classic and contextual bandits"
    - "rawlsian fairness for machine learning"
    - "taming the monster a fast and simple algorithm for contextual bandits"
    - "a contextual bandit approach to personalized news article recommendation"
    - "smoothed analysis of algorithms why the simplex algorithm usually takes polynomial time"

Connected through hyperedge HE-7 (induced by source paper "downstream effects of affirmative action"):
  with members:
    - "delayed impact of fair machine learning"
    - "a short term intervention for long term fairness in the labor market"
    - "fairness in reinforcement learning"
    - "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 "incentivizing exploration with unbiased histories"):
  with members:
    - "a smoothed analysis of the greedy algorithm for the linear contextual bandit problem"
    - "competing bandits learning under competition"
    - "optimal and myopic information acquisition"
    - "bayesian exploration incentivizing exploration in bayesian games"

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.GT(Computer Science and Game Theory)