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 "online learning sensing matrix and sparsifying dictionary simultaneously for compressive sensing"):
  Abstract: This paper considers the problem of simultaneously learning the Sensing Matrix and Sparsifying Dictionary (SMSD) on a large training dataset. To address the formulated joint learning problem, we propose an online algorithm that consists of a closed-form solution for optimizing the sensing matrix with a fixed sparsifying dictionary and a stochastic method for learning the sparsifying dictionary on a large dataset when the sensing matrix is given. Benefiting from training on a large dataset, the obtained compressive sensing (CS) system by the proposed algorithm yields a much better performance in terms of signal recovery accuracy than the existing ones. The simulation results on natural images demonstrate the effectiveness of the suggested online algorithm compared with the existing methods.

Includes members:
  Member "on collaborative compressive sensing systems the framework design and algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "compressed sensing with probability based prior information")
  Member "global optimality in low rank matrix optimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "finding the sparsest vectors in a subspace theory algorithms and applications")
  Member "trainlets dictionary learning in high dimensions":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "dictionary learning for adaptive gpr landmine classification")
  Member "seboost boosting stochastic learning using subspace optimization techniques":
    also connected through other hyperedges:
      (none)
  Member "an efficient method for robust projection matrix design":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online multilinear dictionary learning")
  Member "approximating sampled sinusoids and multiband signals using multiband modulated dpss dictionaries":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "compressed sensing with probability based prior information")
  Member "projection design for statistical compressive sensing a tight frame based approach":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "online multilinear dictionary 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)