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 "a little charity guarantees almost envy freeness"):
  Abstract: Fair division of indivisible goods is a very well-studied problem. The goal of this problem is to distribute $m$ goods to $n$ agents in a "fair" manner, where every agent has a valuation for each subset of goods. We assume general valuations. #R##N#Envy-freeness is the most extensively studied notion of fairness. However, envy-free allocations do not always exist when goods are indivisible. The notion of fairness we consider here is "envy-freeness up to any good" (EFX) where no agent envies another agent after the removal of any single good from the other agent's bundle. It is not known if such an allocation always exists even when $n=3$. #R##N#We show there is always a partition of the set of goods into $n+1$ subsets $(X_1,\ldots,X_n,P)$ where for $i \in [n]$, $X_i$ is the bundle allocated to agent $i$ and the set $P$ is unallocated (or donated to charity) such that we have$\colon$ #R##N#1) envy-freeness up to any good, #R##N#2) no agent values $P$ higher than her own bundle, and #R##N#3) fewer than $n$ goods go to charity, i.e., $|P| < n$ (typically $m \gg n$). #R##N#Our proof is constructive. When agents have additive valuations and $\lvert P \rvert$ is large (i.e., when $|P|$ is close to $n$), our allocation also has a good maximin share (MMS) guarantee. Moreover, a minor variant of our algorithm also shows the existence of an allocation which is $4/7$ groupwise maximin share (GMMS): this is a notion of fairness stronger than MMS. This improves upon the current best bound of $1/2$ known for an approximate GMMS allocation.

Includes members:
  Member "envy freeness up to any item with high nash welfare the virtue of donating items":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "maximum nash welfare and other stories about efx")
  Member "an improved approximation algorithm for maximin shares":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "maximin fairness with mixed divisible and indivisible goods")
  Member "almost envy free allocations with connected bundles":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fairrec two sided fairness for personalized recommendations in two sided platforms")
  Member "on fair division of indivisible items":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efx exists for three agents")
  Member "groupwise maximin fair allocation of indivisible goods":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (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-7 (induced by source paper "efx exists for three agents")
  Member "finding fair and efficient allocations":
    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)