human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "the communication complexity of local search":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "near optimal communication lower bounds for approximate nash equilibria")
  Member "planning with multiple biases":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "time inconsistent planning simple motivation is hard to find")
  Member "on the value of penalties in time inconsistent planning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "time inconsistent planning simple motivation is hard to find")
  Member "the price of uncertainty in present biased planning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "time inconsistent planning simple motivation is hard to find")
  Member "motivating time inconsistent agents a computational approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "time inconsistent planning simple motivation is hard to find")
  Member "procrastination with variable present bias":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "penalty bidding mechanisms for allocating resources and overcoming present bias")
  Member "planning problems for sophisticated agents with present bias":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "time inconsistent planning simple motivation is hard to find")
  Member "computational issues in time inconsistent planning":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "directed graph embeddings")

Question: Please tell me which class the source paper 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)