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 "envy freeness up to any item with high nash welfare the virtue of donating items"):
  Abstract: Several fairness concepts have been proposed recently in attempts to approximate envy-freeness in settings with indivisible goods. Among them, the concept of envy-freeness up to any item (EFX) is arguably the closest to envy-freeness. Unfortunately, EFX allocations are not known to exist except in a few special cases. We make significant progress in this direction. We show that for every instance with additive valuations, there is an EFX allocation of a subset of items with a Nash welfare that is at least half of the maximum possible Nash welfare for the original set of items. That is, after donating some items to a charity, one can distribute the remaining items in a fair way with high efficiency. This bound is proved to be best possible. Our proof is constructive and highlights the importance of maximum Nash welfare allocation. Starting with such an allocation, our algorithm decides which items to donate and redistributes the initial bundles to the agents, eventually obtaining an allocation with the claimed efficiency guarantee. The application of our algorithm to large markets, where the valuations of an agent for every item is relatively small, yields EFX with almost optimal Nash welfare. To the best of our knowledge, this is the first use of large market assumptions in the fair division literature. We also show that our algorithm can be modified to compute, in polynomial-time, EFX allocations that approximate optimal Nash welfare within a factor of at most $2\rho$, using a $\rho$-approximate allocation on input instead of the maximum Nash welfare one.

Includes members:
  Member "comparing approximate relaxations of envy freeness":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "maximin fairness with mixed divisible and indivisible goods")
  Member "on fair division of indivisible items":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efx exists for three agents")
  Member "approximation algorithms for computing maximin share allocations":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "maximin fairness with mixed divisible and indivisible goods")
  Member "almost envy freeness with general valuations":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "equitable allocations of indivisible chores")
  Member "approximating the nash social welfare with budget additive valuations":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efx exists for three agents")
  Member "finding fair and efficient allocations":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "maximum nash welfare and other stories about efx")
  Member "a discrete and bounded envy free cake cutting protocol for any number of agents":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "maximin fairness with mixed divisible and indivisible goods")
  Member "convex program duality fisher markets and nash social welfare":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "maximum nash welfare and other stories about efx")

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)