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 "scalable hyperbolic recommender systems"):
  Abstract: We present a large scale hyperbolic recommender system. We discuss why hyperbolic geometry is a more suitable underlying geometry for many recommendation systems and cover the fundamental milestones and insights that we have gained from its development. In doing so, we demonstrate the viability of hyperbolic geometry for recommender systems, showing that they significantly outperform Euclidean models on datasets with the properties of complex networks. Key to the success of our approach are the novel choice of underlying hyperbolic model and the use of the Einstein midpoint to define an asymmetric recommender system in hyperbolic space. These choices allow us to scale to millions of users and hundreds of thousands of items.

Includes members:
  Member "hyperbolic attention networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "latent variable modelling with hyperbolic normalizing flows")
  Member "skip gram word embeddings in hyperbolic space":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "fine grained entity typing in hyperbolic space")
  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 "poincar e glove hyperbolic word embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "binarized pmi matrix bridging word embeddings and hyperbolic spaces")
  Member "hyperbolic neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "data augmentation with mobius transformations")
  Member "embedding text in hyperbolic spaces":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "latent variable modelling with hyperbolic normalizing flows")
  Member "representation tradeoffs for hyperbolic embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "constant curvature graph convolutional networks")

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)