human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "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.

Includes members:
  Member "on the convergence of adam and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "hyperbolic recommender systems":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning feature interactions with lorentzian factorization machine")
  Member "learning continuous hierarchies in the lorentz model of hyperbolic geometry":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "discriminative topic mining via category name guided text embedding")
  Member "hyperbolic neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "data augmentation with mobius transformations")
  Member "averaging stochastic gradient descent on riemannian manifolds":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "probabilistic permutation synchronization using the riemannian structure of the birkhoff polytope")
  Member "representation tradeoffs for hyperbolic embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "constant curvature graph convolutional networks")
  Member "hyperbolic entailment cones for learning hierarchical embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "discriminative topic mining via category name guided text embedding")
  Member "riemannian approach to batch normalization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "an internal covariate shift bounding algorithm for deep neural networks by unitizing layers outputs")

Question: Please tell me which class the source paper 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)