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 "bounded incentives in manipulating the probabilistic serial rule"):
  Abstract: The Probabilistic Serial mechanism is well-known for its desirable fairness and efficiency properties. It is one of the most prominent protocols for the random assignment problem. However, Probabilistic Serial is not incentive-compatible, thereby these desirable properties only hold for the agents' declared preferences, rather than their genuine preferences. A substantial utility gain through strategic behaviors would trigger self-interested agents to manipulate the mechanism and would subvert the very foundation of adopting the mechanism in practice. In this paper, we characterize the extent to which an individual agent can increase its utility by strategic manipulation. We show that the incentive ratio of the mechanism is $\frac{3}{2}$. That is, no agent can misreport its preferences such that its utility becomes more than 1.5 times of what it is when reports truthfully. This ratio is a worst-case guarantee by allowing an agent to have complete information about other agents' reports and to figure out the best response strategy even if it is computationally intractable in general. To complement this worst-case study, we further evaluate an agent's utility gain on average by experiments. The experiments show that an agent' incentive in manipulating the rule is very limited. These results shed some light on the robustness of Probabilistic Serial against strategic manipulation, which is one step further than knowing that it is not incentive-compatible.

Includes members:
  Member "investigating the characteristics of one sided matching mechanisms under various preferences and risk attitudes":
    also connected through other hyperedges:
      (none)
  Member "manipulating the probabilistic serial rule":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "online fair division a survey")
  Member "social welfare in one sided matching mechanisms":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "average case analysis of the assignment problem with independent preferences")
  Member "an axiomatic approach to characterizing and relaxing strategyproofness of one sided matching mechanisms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "estimating approximate incentive compatibility")
  Member "decision theory with resource bounded agents":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "when humans aren t optimal robots that collaborate with risk aware humans")
  Member "the computational complexity of random serial dictatorship":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "average case analysis of the assignment problem with independent preferences")
  Member "eliminating the weakest link making manipulation intractable":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "x then x manipulation of same system runoff elections")
  Member "social welfare in one sided matching markets without money":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "average case analysis of the assignment problem with independent preferences")

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)