human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "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.

Connected through hyperedge HE-1 (induced by source paper "latent variable modelling with hyperbolic normalizing flows"):
  with members:
    - "adversarial autoencoders with constant curvature latent manifolds"
    - "riemannian normalizing flow on variational wasserstein autoencoder for text modeling"
    - "graph normalizing flows"
    - "mixed curvature variational autoencoders"
    - "poincare wasserstein autoencoder"
    - "hyperbolic graph neural networks"
    - "hyperbolic graph convolutional neural networks"
    - "hyperbolic attention networks"

Connected through hyperedge HE-2 (induced by source paper "metric learning for dynamic text classification"):
  with members:
    - "hyperbolic interaction model for hierarchical multi label classification"
    - "fewrel a large scale supervised few shot relation classification dataset with state of the art evaluation"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "multi source domain adaptation with mixture of experts"
    - "deep contextualized word representations"
    - "hyperbolic neural networks"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "representation tradeoffs for hyperbolic embeddings"

Connected through hyperedge HE-3 (induced by source paper "hyperbolic graph convolutional neural networks"):
  with members:
    - "simplifying graph convolutional networks"
    - "hyperbolic graph neural networks"
    - "hyperbolic attention networks"
    - "link prediction based on graph neural networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "hyperbolic representation learning for fast and efficient neural question answering"
    - "hyperbolic neural networks"

Connected through hyperedge HE-4 (induced by source paper "beyond vector spaces compact data representationas differentiable weighted graphs"):
  with members:
    - "robust graph learning from noisy data"
    - "dag gnn dag structure learning with graph neural networks"
    - "a comprehensive survey on graph neural networks"
    - "hyperbolic recommender systems"
    - "poincar e glove hyperbolic word embeddings"
    - "voxceleb2 deep speaker recognition"
    - "representation tradeoffs for hyperbolic embeddings"
    - "neural collaborative filtering"

Connected through hyperedge HE-5 (induced by source paper "every child should have parents a taxonomy refinement algorithm based on hyperbolic term embeddings"):
  with members:
    - "inferring concept hierarchies from text corpora via hyperbolic embeddings"
    - "taxogen unsupervised topic taxonomy construction by adaptive term embedding and clustering"
    - "improving hypernymy detection with an integrated path based and distributional method"
    - "inriasac simple hypernym extraction methods"
    - "efficient estimation of word representations in vector space"
    - "distributed representations of words and phrases and their compositionality"

Connected through hyperedge HE-6 (induced by source paper "on the compositionality prediction of noun phrases using poincar e embeddings"):
  with members:
    - "every child should have parents a taxonomy refinement algorithm based on hyperbolic term embeddings"
    - "inferring concept hierarchies from text corpora via hyperbolic embeddings"
    - "poincar e embeddings for learning hierarchical representations"
    - "conceptnet 5 5 an open multilingual graph of general knowledge"
    - "non compositional term dependence for information retrieval"
    - "distributed representations of words and phrases and their compositionality"

Question: Please tell me which class the center node 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)