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 "sequential learning of analysis operators"):
  Abstract: In this paper two sequential algorithms for learning analysis operators are presented, which are built upon the same optimisation principle underlying both Analysis K-SVD and Analysis SimCO and use a stochastic gradient descent approach similar to ASimCO. The sequential analysis operator learning (SAOL) algorithm is based on projected gradient descent with an appropriately chosen step size while the implicit SAOL (ISAOL) algorithm avoids choosing a step size altogether by using a strategy inspired by the implicit Euler scheme for solving ordinary differential equations. Both algorithms are tested on synthetic and image data in comparison to Analysis SimCO and found to give slightly better recovery rates resp. decay of the objective function. In a final denoising experiment the presented algorithms are again shown to perform well in comparison to the state of the art algorithm ASimCO.

Includes members:
  Member "learning co sparse analysis operators with separable structures":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "convolutional analysis operator learning dependence on training data")
  Member "convergence radius and sample complexity of itkm algorithms for dictionary learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "generalization error in deep learning")
  Member "local identification of overcomplete dictionaries":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "minimax lower bounds on dictionary learning for tensor data")
  Member "analysis operator learning and its application to image reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "controlling neural networks via energy dissipation")

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)