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 to rank using localized geometric mean metrics"):
  Abstract: Many learning-to-rank~(LtR) algorithms focus on query-independent model, in which query and document do not lie in the same feature space, and the rankers rely on the feature ensemble about query-document pair instead of the similarity between query instance and documents. However, existing algorithms do not consider local structures in query-document feature space, and are fragile to irrelevant noise features. In this paper, we propose a novel Riemannian metric learning algorithm to capture the local structures and develop a robust LtR algorithm. First, we design a concept called ideal candidate document to introduce metric learning algorithm to query-independent model. Previous metric learning algorithms aiming to find an optimal metric space are only suitable for query-dependent model, in which query instance and documents belong to the same feature space and the similarity is directly computed from the metric space. Then we extend the new and extremely fast global Geometric Mean Metric Learning (GMML) algorithm to develop a localized GMML, namely L-GMML. Based on the combination of local learned metrics, we employ the popular Normalized Discounted Cumulative Gain~(NDCG) scorer and Weighted Approximate Rank Pairwise~(WARP) loss to optimize the ideal candidate document for each query candidate set. Finally, we can quickly evaluate all candidates via the similarity between the ideal candidate document and other candidates. By leveraging the ability of metric learning algorithms to describe the complex structural information, our approach gives us a principled and efficient way to perform LtR tasks. The experiments on real-world datasets demonstrate that our proposed L-GMML algorithm outperforms the state-of-the-art metric learning to rank methods and the stylish query-independent LtR algorithms regarding accuracy and computational efficiency.

Includes members:
  Member "similarity learning for high dimensional sparse data":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "fast low rank metric learning for large scale and high dimensional data")
  Member "parametric local metric learning for nearest neighbor classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "local distance metric learning for nearest neighbor algorithm")
  Member "the greedy miser learning under test time budgets":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "interpretable time budget constrained contextualization for re ranking")
  Member "learning content similarity for music recommendation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an empirical study on learning fairness metrics for compas data with human supervision")
  Member "positive semidefinite metric learning using boosting like algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep metric learning with bier boosting independent embeddings robustly")

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.IR(Information Retrieval)