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 graph neural networks"
  Abstract: Learning from graph-structured data is an important task in machine learning and artificial intelligence, for which Graph Neural Networks (GNNs) have shown great promise. Motivated by recent advances in geometric representation learning, we propose a novel GNN architecture for learning representations on Riemannian manifolds with differentiable exponential and logarithmic maps. We develop a scalable algorithm for modeling the structural properties of graphs, comparing Euclidean and hyperbolic geometry. In our experiments, we show that hyperbolic GNNs can lead to substantial improvements on various benchmark datasets.

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 image embeddings"
    - "hyperbolic graph convolutional neural networks"
    - "hyperbolic attention networks"

Connected through hyperedge HE-2 (induced by source paper "constant curvature graph convolutional networks"):
  with members:
    - "deepsphere towards an equivariant graph based spherical cnn"
    - "poincare wasserstein autoencoder"
    - "hyperbolic graph convolutional neural networks"
    - "a comprehensive survey on graph neural networks"
    - "hyperbolic attention networks"
    - "large margin classification in hyperbolic space"
    - "link prediction based on graph neural networks"
    - "predict then propagate graph neural networks meet personalized pagerank"

Connected through hyperedge HE-3 (induced by source paper "hyperbolic graph convolutional neural networks"):
  with members:
    - "simplifying graph convolutional networks"
    - "hyperbolic image embeddings"
    - "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 "hyperbolic graph attention network"):
  with members:
    - "session based social recommendation via dynamic graph attention networks"
    - "constant curvature graph convolutional networks"
    - "hyperbolic graph convolutional neural networks"
    - "graph convolutional neural networks for web scale recommender systems"
    - "hyperbolic neural networks"
    - "representation tradeoffs for hyperbolic embeddings"
    - "hyperbolic entailment cones for learning hierarchical embeddings"
    - "geometric deep learning going beyond euclidean data"

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.LG(Machine Learning)