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: "optimal provision after wait in healthcare"
  Abstract: We investigate computational and mechanism design aspects of scarce resource allocation, where the primary rationing mechanism is through waiting times. Specifically we consider allocating medical treatments to a population of patients. Each patient needs exactly one treatment, and can choose from $k$ hospitals. Hospitals have different costs, which are fully paid by a third party ---the "payer". The payer has a fixed budget $B$, and each hospital will have its own waiting time. At equilibrium, each patient will choose his most preferred hospital given his intrinsic preferences and the waiting times. The payer thus computes the waiting times so that at equilibrium the budget constraint is satisfied and the social welfare is maximized. #R##N#We first show that the optimization problem is NP-hard, yet if the budget can be relaxed to $(1+\epsilon)B$ for an arbitrarily small $\epsilon$, then the optimum under budget $B$ can be approximated efficiently. Next, we study the endogenous emergence of waiting time from the dynamics between hospitals and patients, and show that there is no need for the payer to explicitly enforce the optimal waiting times. Under certain conditions, all he need is to enforce the amount of money he wants to pay to each hospital. The dynamics will always converge to the desired waiting times in finite time. #R##N#We then go beyond equilibrium solutions and investigate the optimization problem over a much larger class of mechanisms containing the equilibrium ones as special cases. With two hospitals, we show that under a natural assumption on the patients' preference profiles, optimal welfare is in fact attained by the randomized assignment mechanism, which allocates patients to hospitals at random subject to the budget constraint, but avoids waiting times. #R##N#Finally, we discuss potential policy implications of our results, as well as follow-up directions and open problems.

Connected through hyperedge HE-1 (induced by source paper "do prices coordinate markets"):
  with members:
    - "the sample complexity of revenue maximization"
    - "the optimal sample complexity of pac learning"
    - "ironing in the dark"
    - "learning economic parameters from revealed preferences"
    - "sampling and representation complexity of revenue maximization"
    - "optimal learners for multiclass problems"
    - "making the most of your samples"
    - "ascending auctions and walrasian equilibrium"

Connected through hyperedge HE-2 (induced by source paper "truthful multi unit procurements with budgets"):
  with members:
    - "mechanism design for crowdsourcing an optimal 1 1 e competitive budget feasible mechanism for large markets"
    - "clinching auctions beyond hard budget constraints"
    - "online submodular welfare maximization greedy is optimal"
    - "budget feasible mechanism design from prior free to bayesian"
    - "mechanism design without money via stable matching"
    - "optimization with demand oracles"
    - "on the approximability of budget feasible mechanisms"
    - "frugal mechanism design via spectral techniques"

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)