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 "finding the sparsest vectors in a subspace theory algorithms and applications"):
  Abstract: The problem of finding the sparsest vector (direction) in a low dimensional subspace can be considered as a homogeneous variant of the sparse recovery problem, which finds applications in robust subspace recovery, dictionary learning, sparse blind deconvolution, and many other problems in signal processing and machine learning. However, in contrast to the classical sparse recovery problem, the most natural formulation for finding the sparsest vector in a subspace is usually nonconvex. In this paper, we overview recent advances on global nonconvex optimization theory for solving this problem, ranging from geometric analysis of its optimization landscapes, to efficient optimization algorithms for solving the associated nonconvex optimization problem, to applications in machine intelligence, representation learning, and imaging sciences. Finally, we conclude this review by pointing out several interesting open problems for future research.

Includes members:
  Member "complete dictionary learning via 120001 4 norm maximization over the orthogonal group":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "complete dictionary learning via ell_p norm maximization")
  Member "nonconvex optimization meets low rank matrix factorization an overview":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a deterministic convergence framework for exact non convex phase retrieval")
  Member "nonconvex robust low rank matrix recovery":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "subgradient descent learns orthogonal dictionaries")
  Member "dual principal component pursuit probability analysis and efficient algorithms":
    also connected through other hyperedges:
      (none)
  Member "global geometry of multichannel sparse blind deconvolution on the sphere":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "complete dictionary learning via ell_p norm maximization")
  Member "an overview of robust subspace recovery":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "list decodable subspace recovery via sum of squares")
  Member "subgradient descent learns orthogonal dictionaries":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "complete dictionary learning via ell_p norm maximization")
  Member "complete dictionary recovery over the sphere i overview and the geometric picture":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "complete dictionary learning via ell_p norm maximization")

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)