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: "combinatorial auctions with endowment effect"
  Abstract: We study combinatorial auctions with bidders that exhibit endowment effect. In most of the previous work on cognitive biases in algorithmic game theory (e.g., [Kleinberg and Oren, EC'14] and its follow-ups) the focus was on analyzing the implications and mitigating their negative consequences. In contrast, in this paper we show how in some cases cognitive biases can be harnessed to obtain better outcomes. #R##N#Specifically, we study Walrasian equilibria in combinatorial markets. It is well known that Walrasian equilibria exist only in limited settings, e.g., when all valuations are gross substitutes, but fails to exist in more general settings, e.g., when the valuations are submodular. We consider combinatorial settings in which bidders exhibit the endowment effect, that is, their value for items increases with ownership. #R##N#Our main result shows that when the valuations are submodular, even a mild degree of endowment effect is sufficient to guarantee the existence of Walrasian equilibria. In fact, we show that in contrast to Walrasian equilibria with standard utility maximizing bidders -- in which the equilibrium allocation must be efficient -- when bidders exhibit endowment effect any local optimum can be an equilibrium allocation. #R##N#Our techniques reveal interesting connections between the LP relaxation of combinatorial auctions and local maxima. We also provide lower bounds on the intensity of the endowment effect that the bidders must have in order to guarantee the existence of a Walrasian equilibrium in various settings.

Connected through hyperedge HE-1 (induced by source paper "on the power and limits of dynamic pricing in combinatorial markets"):
  with members:
    - "a general framework for endowment effects in combinatorial markets"
    - "max min greedy matching"
    - "pricing for online resource allocation intervals and paths"
    - "pricing multi unit markets"
    - "makespan minimization via posted prices"
    - "price doubling and item halving robust revenue guarantees for item pricing"
    - "prophet inequalities made easy stochastic optimization by pricing non stochastic inputs"
    - "do prices coordinate markets"

Connected through hyperedge HE-2 (induced by source paper "a general framework for endowment effects in combinatorial markets"):
  with members:
    - "on welfare approximation and stable pricing"
    - "clearing markets via bundles"
    - "welfare and revenue guarantees for competitive bundling equilibrium"
    - "combinatorial walrasian equilibrium"
    - "combinatorial auctions with decreasing marginal utilities"

Connected through hyperedge HE-3 (induced by source paper "the communication complexity of local search"):
  with members:
    - "near optimal communication lower bounds for approximate nash equilibria"
    - "query to communication lifting for bpp"
    - "lifting randomized query complexity to randomized communication complexity"
    - "communication complexity of approximate nash equilibria"
    - "communication lower bounds via critical block sensitivity"

Connected through hyperedge HE-4 (induced by source paper "quality of local equilibria in discrete exchange economies"):
  with members:
    - "the communication complexity of local search"
    - "combinatorial auctions via posted prices"
    - "combinatorial auctions with decreasing marginal utilities"

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)