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 multilinear dictionary learning"):
  Abstract: A method for online tensor dictionary learning is proposed. With the assumption of separable dictionaries, tensor contraction is used to diminish a $N$-way model of $\mathcal{O}\left(L^N\right)$ into a simple matrix equation of $\mathcal{O}\left(NL^2\right)$ with a real-time capability. To avoid numerical instability due to inversion of sparse matrix, a class of stochastic gradient with memory is formulated via a least-square solution to guarantee convergence and robustness. Both gradient descent with exact line search and Newton's method are discussed and realized. Extensions onto how to deal with bad initialization and outliers are also explained in detail. Experiments on two synthetic signals confirms an impressive performance of our proposed method.

Includes members:
  Member "joint sensing matrix and sparsifying dictionary optimization for tensor compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "compressed sensing with probability based prior information")
  Member "an efficient method for robust projection matrix design":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "online learning sensing matrix and sparsifying dictionary simultaneously for compressive sensing")
  Member "tensor decompositions for signal processing applications from two way to multiway component analysis":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "tensor decompositions in deep learning")
  Member "separable dictionary learning":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "learning mixtures of separable dictionaries for tensor data analysis and algorithms")
  Member "projection design for statistical compressive sensing a tight frame based approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "joint sensing matrix and sparsifying dictionary optimization for tensor compressive sensing")
  Member "representation learning a review and new perspectives":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "nestedvae isolating common factors via weak supervision")

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)