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: "on the role of a market maker in networked cournot competition"
  Abstract: We study the role of a market maker (or market operator) in a transmission constrained electricity market. We model the market as a one-shot networked Cournot competition where generators supply quantity bids and load serving entities provide downward sloping inverse demand functions. This mimics the operation of a spot market in a deregulated market structure. In this paper, we focus on possible mechanisms employed by the market maker to balance demand and supply. In particular, we consider three candidate objective functions that the market maker optimizes - social welfare, residual social welfare, and consumer surplus. We characterize the existence of Generalized Nash Equilibrium (GNE) in this setting and demonstrate that market outcomes at equilibrium can be very different under the candidate objective functions.

Connected through hyperedge HE-1 (induced by source paper "no regret learning in cournot games"):
  with members:
    - "introduction to online convex optimization"
    - "finding friend and foe in multi agent games"
    - "bandit learning in concave n person games"
    - "a unified game theoretic approach to multiagent reinforcement learning"
    - "learning in games robustness of fast convergence"
    - "finding any nontrivial coarse correlated equilibrium is hard"
    - "fast convergence of regularized learning in games"
    - "online convex optimization in the bandit setting gradient descent without a gradient"

Connected through hyperedge HE-2 (induced by source paper "on the equilibria and efficiency of electricity markets with renewable power producers and congestion constraints"):
  with members:
    - "indirect mechanism design for efficient and stable renewable energy aggregation"
    - "a structural characterization of market power in electric power networks"

Connected through hyperedge HE-3 (induced by source paper "the price of fragmentation in mobility on demand services"):
  with members:
    - "pricing and optimization in shared vehicle systems an approximation framework"
    - "network cournot competition"
    - "price competition in networked markets how do monopolies impact social welfare"
    - "asymptotically optimal algorithms for one to one pickup and delivery problems with applications to transportation systems"

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)