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: "a stackelberg strategy for routing flow over time"
  Abstract: Routing games are used to to understand the impact of individual users' decisions on network efficiency. Most prior work on routing games uses a simplified model of network flow where all flow exists simultaneously, and users care about either their maximum delay or their total delay. Both of these measures are surrogates for measuring how long it takes to get all of a user's traffic through the network. We attempt a more direct study of how competition affects network efficiency by examining routing games in a flow over time model. We give an efficiently computable Stackelberg strategy for this model and show that the competitive equilibrium under this strategy is no worse than a small constant times the optimal, for two natural measures of optimality.

Connected through hyperedge HE-1 (induced by source paper "computation of dynamic equilibria in series parallel networks"):
  with members:
    - "multi source multi sink nash flows over time"
    - "nash flows over time with spillback"

Connected through hyperedge HE-2 (induced by source paper "algorithms for flows over time with scheduling costs"):
  with members:
    - "pricing in resource allocation games based on duality gaps"
    - "nash flows over time with spillback"
    - "the simplex algorithm is np mighty"

Connected through hyperedge HE-3 (induced by source paper "dynamic flows with adaptive route choice"):
  with members:
    - "multi source multi sink nash flows over time"
    - "routing games over time with fifo policy"

Connected through hyperedge HE-4 (induced by source paper "route cost assignment with joint user and operator behavior as a many to one stable matching assignment game"):
  with members:
    - "fair cost allocation for ridesharing services modeling mathematical programming and an algorithm to find the nucleolus"
    - "network learning via multiagent inverse transportation problems"
    - "a decomposition algorithm to solve the multi hop peer to peer ride matching problem"
    - "the costs and benefits of ridesharing sequential individual rationality and sequential fairness"

Connected through hyperedge HE-5 (induced by source paper "routing games over time with fifo policy"):
  with members:
    - "a network game of dynamic traffic"
    - "dynamic atomic congestion games with seasonal flows"
    - "braess s paradox for flows over time"

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)