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: "trade offs in school choice comparing deferred acceptance the naive and the classic boston mechanism"
  Abstract: The three most common school choice mechanisms are the Deferred Acceptance mechanism (DA), the classic Boston mechanism (BM), and a variant of the Boston mechanism where students automatically skip exhausted schools, which we call the adaptive Boston mechanism (ABM). Assuming truthful reporting, we compare student welfare under these mechanisms both from a conceptual and from a quantitative perspective: We first show that, BM rank dominates DA whenever they are comparable; and via limit arguments and simulations we show that ABM yields intermediate student welfare between BM and DA. Second, we perform computational experiments with preference data from the high school match in Mexico City. We find that student welfare (in terms of rank transitions) is highest under BM, intermediate under ABM, and lowest under DA. BM, ABM, and DA can thus be understood to form a hierarchy in terms of student welfare. In contrast, in (Mennle and Seuken, 2017), we have found that the same mechanisms also form a hierarchy in terms of incentives for truthtelling that points in the opposite direction. A decision between them therefore involves an implicit trade-off between incentives and student welfare.

Connected through hyperedge HE-1 (induced by source paper "robust multi agent counterfactual prediction"):
  with members:
    - "theory of minds understanding behavior in groups through inverse planning"
    - "deep counterfactual regret minimization"
    - "maintaining cooperation in complex social dilemmas using deep reinforcement learning"
    - "m 3 rl mind aware multi agent management reinforcement learning"
    - "reinforcement learning and inverse reinforcement learning with system 1 and system 2"
    - "optimal auctions through deep learning"
    - "multi agent actor critic for mixed cooperative competitive environments"
    - "deep reinforcement learning from self play in imperfect information 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)