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 attention networks"
  Abstract: We introduce hyperbolic attention networks to endow neural networks with enough capacity to match the complexity of data with hierarchical and power-law structure. A few recent approaches have successfully demonstrated the benefits of imposing hyperbolic geometry on the parameters of shallow networks. We extend this line of work by imposing hyperbolic geometry on the activations of neural networks. This allows us to exploit hyperbolic geometry to reason about embeddings produced by deep networks. We achieve this by re-expressing the ubiquitous mechanism of soft attention in terms of operations defined for hyperboloid and Klein models. Our method shows improvements in terms of generalization on neural machine translation, learning on graphs and visual question answering tasks while keeping the neural representations compact.

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 image embeddings"
    - "hyperbolic graph convolutional neural 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 neural networks"
    - "hyperbolic graph convolutional neural networks"
    - "a comprehensive survey on graph neural 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 "universal transformers"):
  with members:
    - "weighted transformer network for machine translation"
    - "neural models for reasoning over multiple mentions using coreference"
    - "the importance of being recurrent for modeling hierarchical structure"
    - "attention is all you need"
    - "convolutional sequence to sequence learning"
    - "a structured self attentive sentence embedding"
    - "linguistic knowledge as memory for recurrent neural networks"
    - "broad context language modeling as reading comprehension"

Connected through hyperedge HE-4 (induced by source paper "hyperbolic image embeddings"):
  with members:
    - "riemannian adaptive optimization methods"
    - "multi level factorisation net for person re identification"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "poincar e glove hyperbolic word embeddings"
    - "hyperbolic neural networks"
    - "embedding text in hyperbolic spaces"
    - "representation tradeoffs for hyperbolic embeddings"
    - "part aligned bilinear representations for person re identification"

Connected through hyperedge HE-5 (induced by source paper "hyperbolic graph convolutional neural networks"):
  with members:
    - "simplifying graph convolutional networks"
    - "hyperbolic graph neural networks"
    - "hyperbolic image embeddings"
    - "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-6 (induced by source paper "fine grained entity typing in hyperbolic space"):
  with members:
    - "imposing label relational inductive bias for extremely fine grained entity typing"
    - "skip gram word embeddings in hyperbolic space"
    - "hyperbolic representation learning for fast and efficient neural question answering"
    - "ultra fine entity typing"
    - "a hierarchical multi task approach for learning embeddings from semantic tasks"
    - "embedding text in hyperbolic spaces"
    - "representation tradeoffs for hyperbolic embeddings"
    - "hierarchical losses and new resources for fine grained entity typing and linking"

Connected through hyperedge HE-7 (induced by source paper "learning feature interactions with lorentzian factorization machine"):
  with members:
    - "scalable hyperbolic recommender systems"
    - "autoint automatic feature interaction learning via self attentive neural networks"
    - "riemannian adaptive optimization methods"
    - "hyperbolic recommender systems"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "xdeepfm combining explicit and implicit feature interactions for recommender systems"
    - "representation tradeoffs for hyperbolic embeddings"
    - "hyperbolic entailment cones for learning hierarchical embeddings"

Connected through hyperedge HE-8 (induced by source paper "hyperml a boosting metric learning approach in hyperbolic space for recommender systems"):
  with members:
    - "adversarial mahalanobis distance based attentive song recommender for automatic playlist continuation"
    - "scalable hyperbolic recommender systems"
    - "signed distance based deep memory recommender"
    - "neurec on nonlinear transformation for personalized ranking"
    - "riemannian adaptive optimization methods"
    - "latent relational metric learning via memory based attention for collaborative ranking"
    - "outer product based neural collaborative filtering"
    - "hyperbolic representation learning for fast and efficient neural question answering"

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.NE(Neural and Evolutionary Computing)