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 natural dynamics for bargaining on exchange networks"
  Abstract: Bargaining networks model the behavior of a set of players that need to reach pairwise agreements for making profits. Nash bargaining solutions are special outcomes of such games that are both stable and balanced. Kleinberg and Tardos proved a sharp algorithmic characterization of such outcomes, but left open the problem of how the actual bargaining process converges to them. A partial answer was provided by Azar et al. who proposed a distributed algorithm for constructing Nash bargaining solutions, but without polynomial bounds on its convergence rate. In this paper, we introduce a simple and natural model for this process, and study its convergence rate to Nash bargaining solutions. At each time step, each player proposes a deal to each of her neighbors. The proposal consists of a share of the potential profit in case of agreement. The share is chosen to be balanced in Nash's sense as far as this is feasible (with respect to the current best alternatives for both players). We prove that, whenever the Nash bargaining solution is unique (and satisfies a positive gap condition) this dynamics converges to it in polynomial time. Our analysis is based on an approximate decoupling phenomenon between the dynamics on different substructures of the network. This approach may be of general interest for the analysis of local algorithms on networks.

Connected through hyperedge HE-1 (induced by source paper "network bargaining with general capacities"):
  with members:
    - "the cooperative game theory foundations of network bargaining games"

Connected through hyperedge HE-2 (induced by source paper "bargaining dynamics in exchange networks"):
  with members:
    - "convergence of local dynamics to balanced outcomes in exchange networks"
    - "max product for maximum weight matching convergence correctness and lp duality"

Connected through hyperedge HE-3 (induced by source paper "bargaining dynamics in exchange networks"):
  with members:
    - "on oblivious ptas s for nash equilibrium"
    - "belief propagation for min cost network flow convergence and correctness"
    - "an fptas for bargaining networks with unequal bargaining powers"
    - "the cooperative game theory foundations of network bargaining games"
    - "on the deletion channel with small deletion probability"
    - "convergence of local dynamics to balanced outcomes in exchange networks"
    - "max product for maximum weight matching convergence correctness and lp duality"

Connected through hyperedge HE-4 (induced by source paper "the cooperative game theory foundations of network bargaining games"):
  with members:
    - "on oblivious ptas s for nash equilibrium"
    - "convergence of local dynamics to balanced outcomes in exchange networks"
    - "computing nash equilibria approximation and smoothed complexity"
    - "complexity of determining nonemptiness of the core"

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)