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: "representation tradeoffs for hyperbolic embeddings"
  Abstract: Hyperbolic embeddings offer excellent quality with few dimensions when embedding hierarchical data structures like synonym or type hierarchies. Given a tree, we give a combinatorial construction that embeds the tree in hyperbolic space with arbitrarily low distortion without using optimization. On WordNet, our combinatorial embedding obtains a mean-average-precision of 0.989 with only two dimensions, while Nickel et al.'s recent construction obtains 0.87 using 200 dimensions. We provide upper and lower bounds that allow us to characterize the precision-dimensionality tradeoff inherent in any hyperbolic embedding. To embed general metric spaces, we propose a hyperbolic generalization of multidimensional scaling (h-MDS). We show how to perform exact recovery of hyperbolic points from distances, provide a perturbation analysis, and give a recovery result that allows us to reduce dimensionality. The h-MDS approach offers consistently low distortion even with few dimensions across several datasets. Finally, we extract lessons from the algorithms and theory above to design a PyTorch-based implementation that can handle incomplete information and is scalable.

Connected through hyperedge HE-1 (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"
    - "hyperbolic attention networks"
    - "large margin classification in hyperbolic space"
    - "link prediction based on graph neural networks"

Connected through hyperedge HE-2 (induced by source paper "hyperkg hyperbolic knowledge graph embeddings for knowledge base completion"):
  with members:
    - "rotate knowledge graph embedding by relational rotation in complex space"
    - "tucker tensor factorization for knowledge graph completion"
    - "inferring concept hierarchies from text corpora via hyperbolic embeddings"
    - "multi relational poincar e graph embeddings"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "poincar e glove hyperbolic word embeddings"
    - "hyperbolic neural networks"
    - "from knowledge graph embedding to ontology embedding an analysis of the compatibility between vector space representations and rules"

Connected through hyperedge HE-3 (induced by source paper "hyperbolic graph neural networks"):
  with members:
    - "hyperbolic graph convolutional neural networks"
    - "link prediction based on graph neural networks"
    - "riemannian adaptive optimization methods"
    - "graph convolutional neural networks for web scale recommender systems"
    - "constrained graph variational autoencoders for molecule design"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "hyperbolic neural networks"
    - "modeling polypharmacy side effects with graph convolutional networks"

Connected through hyperedge HE-4 (induced by source paper "hyperbolic image embeddings"):
  with members:
    - "hyperbolic attention networks"
    - "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"
    - "part aligned bilinear representations for person re identification"

Connected through hyperedge HE-5 (induced by source paper "metric learning for dynamic text classification"):
  with members:
    - "hyperbolic interaction model for hierarchical multi label classification"
    - "hyperbolic image embeddings"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "mixed curvature variational autoencoders"):
  with members:
    - "constant curvature graph convolutional networks"
    - "riemannian adaptive optimization methods"
    - "neural autoregressive flows"
    - "challenging common assumptions in the unsupervised learning of disentangled representations"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "graphvae towards generation of small graphs using variational autoencoders"
    - "hyperbolic neural networks"
    - "spherical latent spaces for stable variational autoencoders"

Connected through hyperedge HE-7 (induced by source paper "hyperbolic graph convolutional neural networks"):
  with members:
    - "simplifying graph convolutional networks"
    - "hyperbolic graph neural 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"

Connected through hyperedge HE-8 (induced by source paper "fine grained entity typing in hyperbolic space"):
  with members:
    - "imposing label relational inductive bias for extremely fine grained entity typing"
    - "hyperbolic attention networks"
    - "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"
    - "hierarchical losses and new resources for fine grained entity typing and linking"

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)