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 stochastic variance reduced gradient algorithm with retraction and vector transport"
  Abstract: In recent years, stochastic variance reduction algorithms have attracted considerable attention for minimizing the average of a large but finite number of loss functions. This paper proposes a nove...

Connected through hyperedge HE-1 (induced by source paper "adaptive stochastic gradient algorithms on riemannian manifolds"):
  with members:
    - "on the convergence of adam and beyond"
    - "on the convergence of adaptive gradient methods for nonconvex optimization"
    - "riemannian adaptive optimization methods"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "averaging stochastic gradient descent on riemannian manifolds"
    - "on the convergence of a class of adam type algorithms for non convex optimization"
    - "hyperbolic entailment cones for learning hierarchical embeddings"
    - "riemannian approach to batch normalization"

Connected through hyperedge HE-2 (induced by source paper "cheap orthogonal constraints in neural networks a simple parametrization of the orthogonal and unitary group"):
  with members:
    - "riemannian adaptive optimization methods"
    - "can we gain more from orthogonality regularizations in training deep cnns"
    - "orthogonal weight normalization solution to optimization over multiple dependent stiefel manifolds in deep neural networks"
    - "on orthogonality and learning recurrent networks with long term dependencies"
    - "generalized backpropagation etude de cas orthogonality"
    - "efficient orthogonal parametrisation of recurrent neural networks using householder reflections"
    - "recurrent orthogonal networks and long memory tasks"
    - "tunable efficient unitary neural networks eunn and their application to rnns"

Connected through hyperedge HE-3 (induced by source paper "averaging stochastic gradient descent on riemannian manifolds"):
  with members:
    - "complete dictionary recovery over the sphere ii recovery by riemannian trust region method"
    - "streaming pca matching matrix bernstein and near optimal finite sample guarantees for oja s algorithm"
    - "convergence of stochastic gradient descent for pca"
    - "fast stochastic algorithms for svd and pca convergence properties and convexity"

Connected through hyperedge HE-4 (induced by source paper "a riemannian gossip approach to subspace learning on grassmann manifold"):
  with members:
    - "a survey on multi task learning"
    - "dimensionality reduction on spd manifolds the emergence of geometry aware methods"
    - "a riemannian gossip approach to decentralized matrix completion"
    - "manopt a matlab toolbox for optimization on manifolds"
    - "a convex formulation for learning task relationships in multi task learning"
    - "fixed rank matrix factorizations and riemannian low rank optimization"
    - "a convex feature learning formulation for latent task structure discovery"
    - "learning task grouping and overlap in multi task learning"

Connected through hyperedge HE-5 (induced by source paper "riemannian stochastic quasi newton algorithm with variance reduction and its convergence analysis"):
  with members:
    - "low rank tensor completion a riemannian manifold preconditioning approach"
    - "kernel methods on riemannian manifolds with gaussian rbf kernels"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"
    - "manopt a matlab toolbox for optimization on manifolds"

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)