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: "the cooperative game theory foundations of network bargaining games"
  Abstract: We study bargaining games between suppliers and manufacturers in a network context. Agents wish to enter into contracts in order to generate surplus which then must be divided among the participants. Potential contracts and their surplus are represented by weighted edges in our bipartite network. Each agent in the market is additionally limited by a capacity representing the number of contracts which he or she may undertake. When all agents are limited to just one contract each, prior research applied natural generalizations of the Nash bargaining solution to the networked setting, defined the new solution concepts of stable and balanced, and characterized the resulting bargaining outcomes. We simplify and generalize these results to a setting in which participants in only one side of the market are limited to one contract each. The heart of our results uses a linear-programming formulation to establish a novel connection between well-studied cooperative game theory concepts (such as core and prekernel) and the solution concepts of stable and balanced defined for the bargaining games. This immediately implies one can take advantage of the results and algorithms in cooperative game theory to reproduce results such as those of Azar et al. [1] and Kleinberg and Tardos [29] and also generalize them to our setting. The cooperative-game-theoretic connection also inspires us to refine our solution space using standard solution concepts from that literature such as nucleolus and lexicographic kernel. The nucleolus is particularly attractive as it is unique, always exists, and is supported by experimental data in the network bargaining literature. Guided by algorithms from cooperative game theory, we show how to compute the nucleolus by pruning and iteratively solving a natural linear-programming formulation.

Connected through hyperedge HE-1 (induced by source paper "cost sharing in two sided markets"):
  with members:
    - "approximating gains from trade in two sided markets via simple mechanisms"
    - "two sided facility location"
    - "combinatorial cost sharing"
    - "fixed price approximability of the optimal gain from trade"
    - "cost recovering bayesian algorithmic mechanism design"

Connected through hyperedge HE-2 (induced by source paper "computing the nucleolus of weighted cooperative matching games in polynomial time"):
  with members:
    - "the stable fixtures problem with payments"
    - "the matching polytope has exponential extension complexity"

Connected through hyperedge HE-3 (induced by source paper "additive stabilizers for unstable graphs"):
  with members:
    - "almost polynomial ratio eth hardness of approximating densest k subgraph"
    - "analytical approach to parallel repetition"
    - "network bargaining using approximate blocking sets to stabilize unstable instances"

Connected through hyperedge HE-4 (induced by source paper "how good is bargained routing"):
  with members:
    - "network non neutrality through preferential signaling"

Connected through hyperedge HE-5 (induced by source paper "network cournot competition"):
  with members:
    - "network bargaining with general capacities"

Connected through hyperedge HE-6 (induced by source paper "incentives and efficiency in uncertain collaborative environments"):
  with members:
    - "bayesian games and the smoothness framework"
    - "crowdsourcing with endogenous entry"
    - "implementing optimal outcomes in social computing a game theoretic approach"
    - "optimal crowdsourcing contests"

Connected through hyperedge HE-7 (induced by source paper "network bargaining with general capacities"):
  with members:
    - "a natural dynamics for bargaining on exchange networks"

Connected through hyperedge HE-8 (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"
    - "on the deletion channel with small deletion probability"
    - "a natural dynamics for bargaining on exchange networks"
    - "convergence of local dynamics to balanced outcomes in exchange networks"
    - "max product for maximum weight matching convergence correctness and lp duality"

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)