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 "justified representation in approval based committee voting"):
  Abstract: We consider approval-based committee voting, i.e. the setting where each voter approves a subset of candidates, and these votes are then used to select a fixed-size set of winners (committee). We propose a natural axiom for this setting, which we call justified representation (JR). This axiom requires that if a large enough group of voters exhibits agreement by supporting the same candidate, then at least one voter in this group has an approved candidate in the winning committee. We show that for every list of ballots it is possible to select a committee that provides JR. However, it turns out that several prominent approval-based voting rules may fail to output such a committee. In particular, while Proportional Approval Voting (PAV) always outputs a committee that provides JR, Reweighted Approval Voting (RAV), a tractable approximation to PAV, does not have this property. We then introduce a stronger version of the JR axiom, which we call extended justified representation (EJR), and show that PAV satisfies EJR, while other rules we consider do not; indeed, EJR can be used to characterize PAV within the class of weighted PAV rules. We also consider several other questions related to JR and EJR, including the relationship between JR/EJR and core stability, and the complexity of the associated algorithmic problems.

Includes members:
  Member "proportional justified representation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "group fairness in committee selection")
  Member "structure in dichotomous preferences":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "on the complexity of constructive control under nearly single peaked preferences")
  Member "properties of multiwinner voting rules":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "who is in your top three optimizing learning in elections with many candidates")
  Member "ptas for minimax approval voting":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "on choosing committees based on approval votes in the presence of outliers")
  Member "finding a collective set of items from proportional multirepresentation to group recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "evaluating approval based multiwinner voting in terms of robustness to noise")
  Member "computational aspects of multi winner approval voting":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "evaluating approval based multiwinner voting in terms of robustness to noise")
  Member "achieving fully proportional representation approximability results":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "representing the insincere strategically robust proportional representation")
  Member "on the computation of fully proportional representation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the complexity of constructive control under nearly single peaked preferences")

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.MA(Multiagent Systems)