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 "scalable nuclear norm minimization by subspace pursuit proximal riemannian gradient"):
  Abstract: Nuclear-norm regularization plays a vital role in many learning tasks, such as low-rank matrix recovery (MR), and low-rank representation (LRR). Solving this problem directly can be computationally expensive due to the unknown rank of variables or large-rank singular value decompositions (SVDs). To address this, we propose a proximal Riemannian gradient (PRG) scheme which can efficiently solve trace-norm regularized problems defined on real-algebraic variety $\mMLr$ of real matrices of rank at most $r$. Based on PRG, we further present a simple and novel subspace pursuit (SP) paradigm for general trace-norm regularized problems without the explicit rank constraint $\mMLr$. The proposed paradigm is very scalable by avoiding large-rank SVDs. Empirical studies on several tasks, such as matrix completion and LRR based subspace clustering, demonstrate the superiority of the proposed paradigms over existing methods.

Includes members:
  Member "robust recovery of subspace structures by low rank representation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "gated fusion network for degraded image super resolution")
  Member "efficient and practical stochastic subgradient descent for nuclear norm regularization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sum suboptimal unitary multi task learning framework for spatiotemporal data prediction")
  Member "a hybrid algorithm for convex semidefinite optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "bilinear factorization for low rank sdp learning")
  Member "a riemannian geometry for low rank matrix completion":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "accelerated alternating projections for robust principal component analysis")
  Member "large scale convex minimization with a low rank constraint":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "introduction to online convex optimization")
  Member "low rank matrix recovery from errors and erasures":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "provable subspace tracking from missing data and matrix completion")
  Member "tight oracle bounds for low rank matrix recovery from a minimal number of random measurements":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "provable meta learning of linear representations")
  Member "matrix completion from noisy entries":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "matrix completion with prior subspace information via maximizing correlation")

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)