human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "mechanism design with predicted task revenue for bike sharing systems"):
  Abstract: Bike sharing systems have been widely deployed around the world in recent years. A core problem in such systems is to reposition the bikes so that the distribution of bike supply is reshaped to better match the dynamic bike demand. When the bike-sharing company or platform is able to predict the revenue of each reposition task based on historic data, an additional constraint is to cap the payment for each task below its predicted revenue. In this paper, we propose an incentive mechanism called FEITE to incentivize users to park bicycles at locations desired by the platform toward rebalancing supply and demand. FEITE possesses four important economic and computational properties such as truthfulness and budget feasibility. Furthermore, we prove that when the payment budget is tight, the overall revenue will still exceed or equal the budget. Otherwise, FEITE achieves 2-approximation as compared to the optimal (revenue-maximizing) solution, which is close to the lower bound of at least $\sqrt{2}$ that we also prove. Using an industrial dataset obtained from a large bike-sharing company, our experiments show that FEITE is effective in rebalancing bike supply and demand and generating high revenue as a result, which outperforms several benchmark mechanisms.

Includes members:
  Member "a deep reinforcement learning framework for rebalancing dockless bike sharing systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a cooperative multi agent reinforcement learning framework for resource balancing in complex logistics network")
  Member "online knapsack problem and budgeted truthful bipartite matching":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an optimal algorithm for online multiple knapsack")
  Member "mechanism design for crowdsourcing an optimal 1 1 e competitive budget feasible mechanism for large markets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "budget feasible mechanism design for non monotone submodular objectives offline and online")
  Member "budget feasible mechanisms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "budget feasible mechanism design for non monotone submodular objectives offline and online")

Question: Please tell me which class the source paper 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)