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 "nash social welfare approximation for strategic agents"):
  Abstract: The fair division of resources is an important age-old problem that has led to a rich body of literature. At the center of this literature lies the question of whether there exist fair mechanisms despite strategic behavior of the agents. A fundamental objective function used for measuring fair outcomes is the Nash social welfare, defined as the geometric mean of the agent utilities. This objective function is maximized by widely known solution concepts such as Nash bargaining and the competitive equilibrium with equal incomes. In this work we focus on the question of (approximately) implementing the Nash social welfare. The starting point of our analysis is the Fisher market, a fundamental model of an economy, whose benchmark is precisely the (weighted) Nash social welfare. We begin by studying two extreme classes of valuations functions, namely perfect substitutes and perfect complements, and find that for perfect substitutes, the Fisher market mechanism has a constant approximation: at most 2 and at least e1e. However, for perfect complements, the Fisher market does not work well, its bound degrading linearly with the number of players. #R##N#Strikingly, the Trading Post mechanism---an indirect market mechanism also known as the Shapley-Shubik game---has significantly better performance than the Fisher market on its own benchmark. Not only does Trading Post achieve an approximation of 2 for perfect substitutes, but this bound holds for all concave utilities and becomes arbitrarily close to optimal for Leontief utilities (perfect complements), where it reaches $(1+\epsilon)$ for every $\epsilon > 0$. Moreover, all the Nash equilibria of the Trading Post mechanism are pure for all concave utilities and satisfy an important notion of fairness known as proportionality.

Includes members:
  Member "convex program duality fisher markets and nash social welfare":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "maximum nash welfare and other stories about efx")
  Member "a discrete and bounded envy free cake cutting protocol for four agents":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "consenus halving does it ever get easier")
  Member "an algorithmic framework for strategic fair division":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "monotonicity and competitive equilibrium in cake cutting")
  Member "incentive compatible two player cake cutting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "truthful fair division without free disposal")
  Member "fair allocation without trade":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient multi resource multi unit vcg auction")
  Member "truthful fair division":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "obvious manipulations in cake cutting")
  Member "nash equilibria in fisher market":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "optimal nash equilibria for bandwidth allocation")
  Member "on allocating goods to maximize fairness":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "weighted completion time minimization for unrelated machines via iterative fair contention resolution")

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)