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 "low rank approximations of hyperbolic embeddings"):
  Abstract: The hyperbolic manifold is a smooth manifold of negative constant curvature. While the hyperbolic manifold is well-studied in the literature, it has gained interest in the machine learning and natural language processing communities lately due to its usefulness in modeling continuous hierarchies. Tasks with hierarchical structures are ubiquitous in those fields and there is a general interest to learning hyperbolic representations or embeddings of such tasks. Additionally, these embeddings of related tasks may also share a low-rank subspace. In this work, we propose to learn hyperbolic embeddings such that they also lie in a low-dimensional subspace. In particular, we consider the problem of learning a low-rank factorization of hyperbolic embeddings. We cast these problems as manifold optimization problems and propose computationally efficient algorithms. Empirical results illustrate the efficacy of the proposed approach.

Includes members:
  Member "scalable hyperbolic recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learning feature interactions with lorentzian factorization machine")
  Member "hyperbolic attention networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "latent variable modelling with hyperbolic normalizing flows")
  Member "hyperbolic recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning feature interactions with lorentzian factorization machine")
  Member "learning continuous hierarchies in the lorentz model of hyperbolic geometry":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "discriminative topic mining via category name guided text embedding")
  Member "hyperbolic representation learning for fast and efficient neural question answering":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a study on efficiency accuracy and document structure for answer sentence selection")
  Member "poincar e glove hyperbolic word embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "binarized pmi matrix bridging word embeddings and hyperbolic spaces")
  Member "hyperbolic neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "data augmentation with mobius transformations")
  Member "embedding text in hyperbolic spaces":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "latent variable modelling with hyperbolic normalizing flows")

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)