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 "approximately stable committee selection"):
  Abstract: In the committee selection problem, we are given $m$ candidates, and $n$ voters. Candidates can have different weights. A committee is a subset of candidates, and its weight is the sum of weights of its candidates. Each voter expresses an ordinal ranking over all possible committees. The only assumption we make on preferences is monotonicity: If $S \subseteq S'$ are two committees, then any voter weakly prefers $S'$ to $S$. We study a general notion of group fairness via stability: A committee of given total weight $K$ is stable if no coalition of voters can deviate and choose a committee of proportional weight, so that all these voters strictly prefer the new committee to the existing one. Extending this notion to approximation, for parameter $c \ge 1$, a committee $S$ of weight $K$ is said to be $c$-approximately stable if for any other committee $S'$ of weight $K'$, the fraction of voters that strictly prefer $S'$ to $S$ is strictly less than $\frac{c K'}{K}$. When $c = 1$, this condition is equivalent to classical core stability. The question we ask is: Does a $c$-approximately stable committee of weight at most any given value $K$ always exist for constant $c$? It is relatively easy to show that there exist monotone preferences for which $c \ge 2$. However, even for simple and widely studied preference structures, a non-trivial upper bound on $c$ has been elusive. In this paper, we show that $c = O(1)$ for all monotone preference structures. Our proof proceeds via showing an existence result for a randomized notion of stability, and iteratively rounding the resulting fractional solution.

Includes members:
  Member "proportionally fair clustering":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a survey on bias and fairness in machine learning")
  Member "group fairness in committee selection":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "approval based apportionment")
  Member "fair knapsack":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "developments in multi agent fair allocation")
  Member "fair allocation of indivisible public goods":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "participatory budgeting models and approaches")
  Member "proportionally representative participatory budgeting axioms and algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "participatory budgeting models and approaches")
  Member "the expanding approvals rule improving proportional representation and monotonicity":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "proportionally representative participatory budgeting with ordinal preferences")
  Member "the core of the participatory budgeting problem":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "participatory budgeting models and approaches")
  Member "properties of multiwinner voting rules":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "who is in your top three optimizing learning in elections with many candidates")

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)