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 "hyperbolic image embeddings"):
  Abstract: Computer vision tasks such as image classification, image retrieval and few-shot learning are currently dominated by Euclidean and spherical embeddings, so that the final decisions about class belongings or the degree of similarity are made using linear hyperplanes, Euclidean distances, or spherical geodesic distances (cosine similarity). In this work, we demonstrate that in many practical scenarios hyperbolic embeddings provide a better alternative.

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 "riemannian adaptive optimization methods":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "efficient riemannian optimization on the stiefel manifold via the cayley transform")
  Member "multi level factorisation net for person re identification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "cross resolution adversarial dual network for person re identification and beyond")
  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.CV(Computer Vision and Pattern Recognition)