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 "a riemannian gossip approach to subspace learning on grassmann manifold"):
  Abstract: In this paper, we focus on subspace learning problems on the Grassmann manifold. Interesting applications in this setting include low-rank matrix completion and low-dimensional multivariate regression, among others. Motivated by privacy concerns, we aim to solve such problems in a decentralized setting where multiple agents have access to (and solve) only a part of the whole optimization problem. The agents communicate with each other to arrive at a consensus, i.e., agree on a common quantity, via the gossip protocol. #R##N#We propose a novel cost function for subspace learning on the Grassmann manifold, which is a weighted sum of several sub-problems (each solved by an agent) and the communication cost among the agents. The cost function has a finite sum structure. In the proposed modeling approach, different agents learn individual local subspace but they achieve asymptotic consensus on the global learned subspace. The approach is scalable and parallelizable. Numerical experiments show the efficacy of the proposed decentralized algorithms on various matrix completion and multivariate regression benchmarks.

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 "adaptive stochastic gradient algorithms on riemannian manifolds")
  Member "a survey on multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "ap mtl attention pruned multi task learning model for real time instrument detection and segmentation in robot assisted surgery")
  Member "dimensionality reduction on spd manifolds the emergence of geometry aware methods":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "hallucinating idt descriptors and i3d optical flow features for action recognition with cnns")
  Member "a riemannian gossip approach to decentralized matrix completion":
    also connected through other hyperedges:
      (none)
  Member "manopt a matlab toolbox for optimization on manifolds":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "joint constellation design for the two user non coherent multiple access channel")
  Member "a convex formulation for learning task relationships in multi task learning":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "a simple general approach to balance task difficulty in multi task learning")
  Member "fixed rank matrix factorizations and riemannian low rank optimization":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "fast and robust spectrally sparse signal recovery a provable non convex approach via robust low rank hankel matrix reconstruction")
  Member "a convex feature learning formulation for latent task structure discovery":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "a survey on multi task learning")

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)