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 "cooperative search games symmetric equilibria robustness and price of anarchy"):
  Abstract: Assume that a treasure is placed in one of $M$ boxes according to a known distribution and that $k$ searchers are searching for it in parallel during $T$ rounds. We study the question of how to incentivize selfish players so that the success probability, namely, the probability that at least one player finds the treasure, would be maximized. We focus on congestion policies $C(s)$ that specify the reward that a player receives if it is one of $s$ players that (simultaneously) find the treasure for the first time. We show that the exclusive policy, in which $C(1)=1$ and $C(s)=0$ for $s>1$, yields a price of anarchy of $(1-(1-{1}/{k})^{k})^{-1}$. This is the best possible price among all symmetric reward mechanisms. For this policy, we also have an explicit description of a symmetric equilibrium, which is in some sense unique, and moreover enjoys the best success probability among all symmetric profiles. For general congestion policies, we show how to polynomially find, for any $\theta>0$, a symmetric multiplicative $(1+\theta)(1+C(k))$-equilibrium. #R##N#Together with a reward policy, a central entity can suggest players to play a particular profile at equilibrium. For such purposes, we advocate the use of symmetric equilibria. Besides being fair, symmetric equilibria can also become highly robust to crashes of players. Indeed, in many cases, despite the fact that some small fraction of players crash, symmetric equilibria remain efficient in terms of their group performances and, at the same time, serve as approximate equilibria. We show that this principle holds for a class of games, which we call monotonously scalable games. This applies in particular to our search game, assuming the natural sharing policy, in which $C(s)=1/s$. For the exclusive policy, this general result does not hold, but we show that the symmetric equilibrium is nevertheless robust under mild assumptions.

Includes members:
  Member "the impact of local information on the performance of multiagent systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "when smoothness is not enough toward exact quantification and optimization of the price of anarchy")
  Member "the ants problem":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi round cooperative search games with multiple players")
  Member "bayesian incentive compatible bandit exploration":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "incentivizing exploration with selective data disclosure")
  Member "online makespan minimization with parallel schedules":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online scheduling with makespan minimization state of the art results research challenges and open problems")
  Member "computer science and game theory a brief survey":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "sok tools for game theoretic models of security for cryptocurrencies")
  Member "a computer scientist looks at game theory":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi round cooperative search games with multiple players")

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)