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 "adaptive stochastic gradient algorithms on riemannian manifolds"):
  Abstract: Adaptive stochastic gradient algorithms in the Euclidean space have attracted much attention lately. Such explorations on Riemannian manifolds, on the other hand, are relatively new, limited, and challenging. This is because of the intrinsic non-linear structure of the underlying manifold and the absence of a canonical coordinate system. In machine learning applications, however, most manifolds of interest are represented as matrices with notions of row and column subspaces. In addition, the implicit manifold-related constraints may also lie on such subspaces. For example, the Grassmann manifold is the set of column subspaces. To this end, such a rich structure should not be lost by transforming matrices into just a stack of vectors while developing optimization algorithms on manifolds. #R##N#We propose novel stochastic gradient algorithms for problems on Riemannian manifolds by adapting the row and column subspaces of gradients. Our algorithms are provably convergent and they achieve the convergence rate of order ${O}(\log (T)/\sqrt{T})$, where $T$ is the number of iterations. Our experiments illustrate that the proposed algorithms outperform existing Riemannian adaptive stochastic algorithms.

Includes members:
  Member "riemannian stochastic variance reduced gradient algorithm with retraction and vector transport":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "cheap orthogonal constraints in neural networks a simple parametrization of the orthogonal and unitary group")
  Member "on the convergence of adam and beyond":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "disentangling adaptive gradient methods from learning rates")
  Member "on the convergence of adaptive gradient methods for nonconvex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "non asymptotic convergence of adam type reinforcement learning algorithms under markovian sampling")
  Member "riemannian adaptive optimization methods":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "efficient riemannian optimization on the stiefel manifold via the cayley transform")
  Member "learning continuous hierarchies in the lorentz model of hyperbolic geometry":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discriminative topic mining via category name guided text embedding")
  Member "averaging stochastic gradient descent on riemannian manifolds":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "probabilistic permutation synchronization using the riemannian structure of the birkhoff polytope")
  Member "on the convergence of a class of adam type algorithms for non convex optimization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "non asymptotic convergence of adam type reinforcement learning algorithms under markovian sampling")
  Member "hyperbolic entailment cones for learning hierarchical embeddings":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "discriminative topic mining via category name guided text embedding")

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)