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 "evaluating approval based multiwinner voting in terms of robustness to noise"):
  Abstract: Approval-based multiwinner voting rules have recently received much attention in the Computational Social Choice literature. Such rules aggregate approval ballots and determine a winning committee of alternatives. To assess effectiveness, we propose to employ new noise models that are specifically tailored for approval votes and committees. These models take as input a ground truth committee and return random approval votes to be thought of as noisy estimates of the ground truth. A minimum robustness requirement for an approval-based multiwinner voting rule is to return the ground truth when applied to profiles with sufficiently many noisy votes. Our results indicate that approval-based multiwinner voting is always robust to reasonable noise. We further refine this finding by presenting a hierarchy of rules in terms of how robust to noise they are.

Includes members:
  Member "a quantitative analysis of multi winner rules":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "who is in your top three optimizing learning in elections with many candidates")
  Member "consistent approval based multi winner rules":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "who is in your top three optimizing learning in elections with many candidates")
  Member "multiwinner approval rules as apportionment methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "approval based apportionment")
  Member "finding a collective set of items from proportional multirepresentation to group recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "computing a small agreeable set of indivisible items")
  Member "justified representation in approval based committee voting":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "participatory budgeting models and approaches")
  Member "computational aspects of multi winner approval voting":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "on the complexity of destructive bribery in approval based multi winner voting")
  Member "a maximum likelihood approach for selecting sets of alternatives":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a quantitative analysis of multi winner rules")
  Member "common voting rules as maximum likelihood estimators":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "election coding for distributed learning protecting signsgd against byzantine attacks")

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.AI(Artificial Intelligence)