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: "averaging stochastic gradient descent on riemannian manifolds"
  Abstract: We consider the minimization of a function defined on a Riemannian manifold $\mathcal{M}$ accessible only through unbiased estimates of its gradients. We develop a geometric framework to transform a sequence of slowly converging iterates generated from stochastic gradient descent (SGD) on $\mathcal{M}$ to an averaged iterate sequence with a robust and fast $O(1/n)$ convergence rate. We then present an application of our framework to geodesically-strongly-convex (and possibly Euclidean non-convex) problems. Finally, we demonstrate how these ideas apply to the case of streaming $k$-PCA, where we show how to accelerate the slow rate of the randomized power method (without requiring knowledge of the eigengap) into a robust algorithm achieving the optimal rate of convergence.

Connected through hyperedge HE-1 (induced by source paper "probabilistic permutation synchronization using the riemannian structure of the birkhoff polytope"):
  with members:
    - "synchronisation of partial multi matchings via non negative factorisations"
    - "ppfnet global context aware local features for robust 3d point matching"
    - "bayesian pose graph optimization via bingham distributions and tempered geodesic mcmc"
    - "orb slam2 an open source slam system for monocular stereo and rgb d cameras"
    - "cad priors for accurate and flexible instance reconstruction"
    - "consistent multiple graph matching with multi layer random walks synchronization"
    - "multi image semantic matching by mining consistent features"
    - "distributable consistent multi object matching"

Connected through hyperedge HE-2 (induced by source paper "adaptive stochastic gradient algorithms on riemannian manifolds"):
  with members:
    - "riemannian stochastic variance reduced gradient algorithm with retraction and vector transport"
    - "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"
    - "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-3 (induced by source paper "some limit properties of markov chains induced by stochastic recursive algorithms"):
  with members:
    - "multi agent reinforcement learning via double averaging primal dual optimization"
    - "regularizing and optimizing lstm language models"
    - "averaged dqn variance reduction and stabilization for deep reinforcement learning"
    - "stop wasting my gradients practical svrg"
    - "saga a fast incremental gradient method with support for non strongly convex composite objectives"

Connected through hyperedge HE-4 (induced by source paper "riemannian adaptive optimization methods"):
  with members:
    - "on the convergence of adam and beyond"
    - "hyperbolic recommender systems"
    - "learning continuous hierarchies in the lorentz model of hyperbolic geometry"
    - "hyperbolic neural networks"
    - "representation tradeoffs for hyperbolic embeddings"
    - "hyperbolic entailment cones for learning hierarchical embeddings"
    - "riemannian approach to batch normalization"
    - "riemannian dictionary learning and sparse coding for positive definite matrices"

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)