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 "who is in your top three optimizing learning in elections with many candidates"):
  Abstract: Elections and opinion polls often have many candidates, with the aim to either rank the candidates or identify a small set of winners according to voters' preferences. In practice, voters do not provide a full ranking; instead, each voter provides their favorite K candidates, potentially in ranked order. The election organizer must choose K and an aggregation rule. #R##N#We provide a theoretical framework to make these choices. Each K-Approval or K-partial ranking mechanism (with a corresponding positional scoring rule) induces a learning rate for the speed at which the election correctly recovers the asymptotic outcome. Given the voter choice distribution, the election planner can thus identify the rate optimal mechanism. Earlier work in this area provides coarse order-of-magnitude guaranties which are not sufficient to make such choices. Our framework further resolves questions of when randomizing between multiple mechanisms may improve learning, for arbitrary voter noise models. #R##N#Finally, we use data from 5 large participatory budgeting elections that we organized across several US cities, along with other ranking data, to demonstrate the utility of our methods. In particular, we find that historically such elections have set K too low and that picking the right mechanism can be the difference between identifying the ultimate winner with only a 80% probability or a 99.9% probability after 400 voters.

Includes members:
  Member "truthful aggregation of budget proposals":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "participatory budgeting models and approaches")
  Member "a framework for approval based budgeting methods":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "proportionally representative participatory budgeting with ordinal preferences")
  Member "designing optimal binary rating systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "designing informative rating systems evidence from an online labor market")
  Member "a quantitative analysis of multi winner rules":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "evaluating approval based multiwinner voting in terms of robustness to noise")
  Member "designing informative rating systems for online platforms evidence from two experiments":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "designing optimal binary rating systems")
  Member "consistent approval based multi winner rules":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "evaluating approval based multiwinner voting in terms of robustness to noise")
  Member "iterative local voting for collective decision making in continuous spaces":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "participatory budgeting models and approaches")
  Member "optimizing positional scoring rules for rank aggregation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "quantifying consensus of rankings based on q support patterns")

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)