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 "optimizing positional scoring rules for rank aggregation"):
  Abstract: Nowadays, several crowdsourcing projects exploit social choice methods for computing an aggregate ranking of alternatives given individual rankings provided by workers. Motivated by such systems, we consider a setting where each worker is asked to rank a fixed (small) number of alternatives and, then, a positional scoring rule is used to compute the aggregate ranking. Among the apparently infinite such rules, what is the best one to use? To answer this question, we assume that we have partial access to an underlying true ranking. Then, the important optimization problem to be solved is to compute the positional scoring rule whose outcome, when applied to the profile of individual rankings, is as close as possible to the part of the underlying true ranking we know. We study this fundamental problem from a theoretical viewpoint and present positive and negative complexity results and, furthermore, complement our theoretical findings with experiments on real-world and synthetic data.

Includes members:
  Member "how effective can simple ordinal peer grading be":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "strategyproof peer selection using randomization partitioning and apportionment")
  Member "simple robust and optimal ranking from pairwise comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "diameter based interactive structure discovery")
  Member "aggregating partial rankings with applications to peer grading in massive online open courses":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "incentivizing effort and precision in peer grading")
  Member "methods for ordinal peer grading":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fast and robust rank aggregation against model misspecification")
  Member "random utility theory for social choice":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "regret minimization in stochastic contextual dueling bandits")
  Member "common voting rules as maximum likelihood estimators":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "evaluating approval based multiwinner voting in terms of robustness to noise")
  Member "determining possible and necessary winners under common voting rules given partial orders":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "the complexity of possible winners on partial chains")
  Member "voting with limited information and many alternatives":
    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)