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: "multi objective pricing for shared vehicle systems"
  Abstract: We propose a framework for data-driven pricing in shared vehicle systems (such as bikesharing and carsharing) in which customers can pick up and drop off vehicles in different locations. This framework provides efficient algorithms with rigorous approximation guarantees for a wide class of objective functions (including welfare and revenue), and under a variety of constraints on the prices. An interesting class of constraints accommodated by this framework includes multi-objective settings in which the goal is to maximize some objective function subject to some lower bound on another objective function. An important representative of this class is the Ramsey pricing problem, i.e. maximize revenue subject to some lower bound on welfare. #R##N#Compared to traditional item-pricing problems, pricing in shared vehicle systems is more challenging due to network externalities, wherein setting prices at one demand node may affect the supply at each other node in the network. To capture the system dynamics, we use a closed queueing model in which exogenous demand (obtained from data) can be modulated through pricing. We achieve our approximation guarantees by first projecting the problem to an infinite-supply setting, deriving optimal prices in this limit, and then bounding the performance of these prices in the finite-vehicle system. Our infinite-to-finite supply reduction is of independent interest since it holds for a broader class of objective functions, and applies even more generally than the pricing problems that we consider.

Connected through hyperedge HE-1 (induced by source paper "complexity stability properties of mixed games and dynamic algorithms and learning in the sharing economy"):
  with members:
    - "strategyproof peer selection using randomization partitioning and apportionment"
    - "approximately efficient two sided combinatorial auctions"
    - "learning theory and algorithms for revenue optimization in second price auctions with reserve"
    - "equilibrium strategy and population size effects in lowest unique bid auctions"

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)