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 "hyperml a boosting metric learning approach in hyperbolic space for recommender systems"):
  Abstract: This paper investigates the notion of learning user and item representations in non-Euclidean space. Specifically, we study the connection between metric learning in hyperbolic space and collaborative filtering by exploring Mobius gyrovector spaces where the formalism of the spaces could be utilized to generalize the most common Euclidean vector operations. Overall, this work aims to bridge the gap between Euclidean and hyperbolic geometry in recommender systems through metric learning approach. We propose HyperML (Hyperbolic Metric Learning), a conceptually simple but highly effective model for boosting the performance. Via a series of extensive experiments, we show that our proposed HyperML not only outperforms their Euclidean counterparts, but also achieves state-of-the-art performance on multiple benchmark datasets, demonstrating the effectiveness of personalized recommendation in hyperbolic geometry.

Includes members:
  Member "adversarial mahalanobis distance based attentive song recommender for automatic playlist continuation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "recommender systems based on generative adversarial networks a problem driven perspective")
  Member "scalable hyperbolic recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning feature interactions with lorentzian factorization machine")
  Member "signed distance based deep memory recommender":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "adversarial mahalanobis distance based attentive song recommender for automatic playlist continuation")
  Member "hyperbolic attention networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "latent variable modelling with hyperbolic normalizing flows")
  Member "neurec on nonlinear transformation for personalized ranking":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "chameleon a deep learning meta architecture for news recommender systems phd thesis")
  Member "riemannian adaptive optimization methods":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "efficient riemannian optimization on the stiefel manifold via the cayley transform")
  Member "latent relational metric learning via memory based attention for collaborative ranking":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lightgcn simplifying and powering graph convolution network for recommendation")
  Member "outer product based neural collaborative filtering":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "sequential recommendation with dual side neighbor based collaborative relation modeling")

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)