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: "riemannian adaptive optimization methods"
  Abstract: Several first order stochastic optimization methods commonly used in the Euclidean domain such as stochastic gradient descent (SGD), accelerated gradient descent or variance reduced methods have already been adapted to certain Riemannian settings. However, some of the most popular of these optimization tools - namely Adam , Adagrad and the more recent Amsgrad - remain to be generalized to Riemannian manifolds. We discuss the difficulty of generalizing such adaptive schemes to the most agnostic Riemannian setting, and then provide algorithms and convergence proofs for geodesically convex objectives in the particular case of a product of Riemannian manifolds, in which adaptivity is implemented across manifolds in the cartesian product. Our generalization is tight in the sense that choosing the Euclidean space as Riemannian manifold yields the same algorithms and regret bounds as those that were already known for the standard algorithms. Experimentally, we show faster convergence and to a lower train loss value for Riemannian adaptive methods over their corresponding baselines on the realistic task of embedding the WordNet taxonomy in the Poincare ball.

Connected through hyperedge HE-1 (induced by source paper "efficient riemannian optimization on the stiefel manifold via the cayley transform"):
  with members:
    - "cheap orthogonal constraints in neural networks a simple parametrization of the orthogonal and unitary group"
    - "can we gain more from orthogonality regularizations in training deep cnns"
    - "riemannian approach to batch normalization"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "dizzyrnn reparameterizing recurrent neural networks for norm preserving backpropagation"
    - "wide residual networks"
    - "reducing overfitting in deep networks by decorrelating representations"
    - "unitary evolution recurrent neural networks"

Connected through hyperedge HE-2 (induced by source paper "computationally tractable riemannian manifolds for graph embeddings"):
  with members:
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "grassmannian learning embedding geometry awareness in shallow and deep learning"
    - "hyperbolic entailment cones for learning hierarchical embeddings"
    - "poincar e embeddings for learning hierarchical representations"
    - "node2vec scalable feature learning for networks"
    - "convolutional neural networks on graphs with fast localized spectral filtering"
    - "complex embeddings for simple link prediction"
    - "deep convolutional networks on graph structured data"

Connected through hyperedge HE-3 (induced by source paper "quaternion equivariant capsule networks for 3d point clouds"):
  with members:
    - "equivariant multi view networks"
    - "spherical cnns on unstructured grids"
    - "spherical regression learning viewpoints surface normals and 3d rotations on n spheres"
    - "gauge equivariant convolutional networks and the icosahedral cnn"
    - "learning an effective equivariant 3d descriptor without supervision"
    - "pointnetlk robust efficient point cloud registration using pointnet"
    - "equivariant neural networks and equivarification"
    - "ppfnet global context aware local features for robust 3d point matching"

Connected through hyperedge HE-4 (induced by source paper "hyperbolic graph neural networks"):
  with members:
    - "hyperbolic graph convolutional neural networks"
    - "link prediction based on graph neural networks"
    - "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"
    - "relational inductive biases deep learning and graph networks"

Connected through hyperedge HE-5 (induced by source paper "hyperbolic image embeddings"):
  with members:
    - "hyperbolic attention networks"
    - "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-6 (induced by source paper "mixed curvature variational autoencoders"):
  with members:
    - "constant curvature graph convolutional networks"
    - "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"
    - "adversarially regularized graph autoencoder for graph embedding"

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"
    - "hyperbolic attention networks"
    - "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"
    - "hyperbolic attention networks"
    - "neurec on nonlinear transformation for personalized ranking"
    - "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.LG(Machine Learning)