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 "learning mixtures of plackett luce models from structured partial orders"):
  Abstract: Mixtures of ranking models have been widely used for heterogeneous preferences. However, learning a mixture model is highly nontrivial, especially when the dataset consists of partial orders. In such cases, the parameter of the model may not be even identifiable. In this paper, we focus on three popular structures of partial orders: ranked top-$l_1$, $l_2$-way, and choice data over a subset of alternatives. We prove that when the dataset consists of combinations of ranked top-$l_1$ and $l_2$-way (or choice data over up to $l_2$ alternatives), mixture of $k$ Plackett-Luce models is not identifiable when $l_1+l_2\le 2k-1$ ($l_2$ is set to $1$ when there are no $l_2$-way orders). We also prove that under some combinations, including ranked top-$3$, ranked top-$2$ plus $2$-way, and choice data over up to $4$ alternatives, mixtures of two Plackett-Luce models are identifiable. Guided by our theoretical results, we propose efficient generalized method of moments (GMM) algorithms to learn mixtures of two Plackett-Luce models, which are proven consistent. Our experiments demonstrate the efficacy of our algorithms. Moreover, we show that when full rankings are available, learning from different marginal events (partial orders) provides tradeoffs between statistical efficiency and computational efficiency.

Includes members:
  Member "a cost effective framework for preference elicitation and aggregation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "approximating voting rules from truncated ballots")
  Member "learning mixtures of plackett luce models":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "choice set optimization under discrete choice models of group decisions")
  Member "top k ranking from pairwise comparisons when spectral ranking is optimal":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ranking a set of objects a graph based least square approach")
  Member "data driven rank breaking for efficient rank aggregation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "regret minimization in stochastic contextual dueling bandits")
  Member "efficient probabilistic inference with partial ranking queries":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "riffled independence for efficient inference with partial rankings")
  Member "active ranking using pairwise comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "learning from noisy similar and dissimilar data")
  Member "restricted strong convexity and weighted matrix completion optimal bounds with noise":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")
  Member "matrix completion from noisy entries":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")

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.LG(Machine Learning)