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 "data driven rank breaking for efficient rank aggregation"):
  Abstract: Rank aggregation systems collect ordinal preferences from individuals to produce a global ranking that represents the social preference. Rank-breaking is a common practice to reduce the computational complexity of learning the global ranking. The individual preferences are broken into pairwise comparisons and applied to efficient algorithms tailored for independent paired comparisons. However, due to the ignored dependencies in the data, naive rank-breaking approaches can result in inconsistent estimates. The key idea to produce accurate and consistent estimates is to treat the pairwise comparisons unequally, depending on the topology of the collected data. In this paper, we provide the optimal rank-breaking estimator, which not only achieves consistency but also achieves the best error bound. This allows us to characterize the fundamental tradeoff between accuracy and complexity. Further, the analysis identifies how the accuracy depends on the spectral gap of a corresponding comparison graph.

Includes members:
  Member "collaboratively learning preferences from ordinal data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "choice set optimization under discrete choice models of group decisions")
  Member "spectral mle top k rank aggregation from pairwise comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ranking a set of objects a graph based least square approach")
  Member "estimation from pairwise comparisons sharp minimax bounds with topology dependence":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ranking a set of objects a graph based least square approach")
  Member "simple robust and optimal ranking from pairwise comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "diameter based interactive structure discovery")
  Member "learning mixed membership mallows models from pairwise comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "supporting hard queries over probabilistic preferences")
  Member "efficient estimation of word representations in vector space":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a survey on contextual embeddings")
  Member "random utility theory for social choice":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "regret minimization in stochastic contextual dueling bandits")
  Member "rank centrality ranking from pair wise comparisons":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "adaptive sampling for coarse ranking")

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)