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: "the naive versus the adaptive boston mechanism"
  Abstract: The Boston mechanism is often criticized for its manipulability and the resulting negative implications for welfare and fairness. Nonetheless, it is one of the most popular school choice mechanisms used in practice. In this paper, we first study the traditional (naive) Boston mechanism (NBM) in a setting with no priority structure and single uniform tie-breaking. We show that it imperfectly rank dominates the strategyproof Random Serial Dictatorship (RSD). We then formalize an adaptive variant of the Boston mechanism (ABM), which is also sometimes used in practice. We show that ABM has significantly better incentive properties than NBM (it is partially strategyproof), while it shares most of NBM's efficiency advantages over RSD as markets get large. However, while a direct efficiency comparison of NBM and ABM via imperfect dominance is inconclusive, numerical simulations suggest that NBM is still somewhat more efficient than ABM, which can be interpreted as the cost of partial strategyproofness one pays when choosing ABM over NBM. Our results highlight the subtle trade-off between efficiency and strategyproofness a market designer must make when choosing between the two Boston mechanisms and RSD.

Connected through hyperedge HE-1 (induced by source paper "an axiomatic approach to characterizing and relaxing strategyproofness of one sided matching mechanisms"):
  with members:
    - "hybrid mechanisms trading off efficiency and strategyproofness in one sided matching"
    - "the computational complexity of random serial dictatorship"
    - "quantifying the strategyproofness of mechanisms via metrics on payoff distributions"

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)