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 "average case analysis of the assignment problem with independent preferences"):
  Abstract: The fundamental assignment problem is in search of welfare maximization mechanisms to allocate items to agents when the private preferences over indivisible items are provided by self-interested agents. The mainstream mechanism \textit{Random Priority} is asymptotically the best mechanism for this purpose, when comparing its welfare to the optimal social welfare using the canonical \textit{worst-case approximation ratio}. Despite its popularity, the efficiency loss indicated by the worst-case ratio does not have a constant bound. Recently, [Deng, Gao, Zhang 2017] show that when the agents' preferences are drawn from a uniform distribution, its \textit{average-case approximation ratio} is upper bounded by 3.718. They left it as an open question of whether a constant ratio holds for general scenarios. In this paper, we offer an affirmative answer to this question by showing that the ratio is bounded by $1/\mu$ when the preference values are independent and identically distributed random variables, where $\mu$ is the expectation of the value distribution. This upper bound also improves the upper bound of 3.718 in [Deng, Gao, Zhang 2017] for the Uniform distribution. Moreover, under mild conditions, the ratio has a \textit{constant} bound for any independent random values. En route to these results, we develop powerful tools to show the insights that in most instances the efficiency loss is small.

Includes members:
  Member "smoothed and average case approximation ratios of mechanisms beyond the worst case analysis":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "smoothed analysis of multi item auctions with correlated values")
  Member "average case approximation ratio of scheduling without payments":
    also connected through other hyperedges:
      (none)
  Member "facility location with double peaked preference":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "the control complexity of r approval from the single peaked case to the general case")
  Member "social welfare in one sided matching mechanisms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bounded incentives in manipulating the probabilistic serial rule")
  Member "an axiomatic approach to characterizing and relaxing strategyproofness of one sided matching mechanisms":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "bounded incentives in manipulating the probabilistic serial rule")
  Member "social welfare in one sided matchings random priority and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "keeping your friends close land allocation with friends")
  Member "the computational complexity of random serial dictatorship":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bounded incentives in manipulating the probabilistic serial rule")
  Member "social welfare in one sided matching markets without money":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "bounded incentives in manipulating the probabilistic serial rule")

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)