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 "joint sensing matrix and sparsifying dictionary optimization for tensor compressive sensing"):
  Abstract: Tensor compressive sensing (TCS) is a multidimensional framework of compressive sensing (CS), and it is advantageous in terms of reducing the amount of storage, easing hardware implementations, and preserving multidimensional structures of signals in comparison to a conventional CS system. In a TCS system, instead of using a random sensing matrix and a predefined dictionary, the average-case performance can be further improved by employing an optimized multidimensional sensing matrix and a learned multilinear sparsifying dictionary. In this paper, we propose an approach that jointly optimizes the sensing matrix and dictionary for a TCS system. For the sensing matrix design in TCS, an extended separable approach with a closed form solution and a novel iterative nonseparable method are proposed when the multilinear dictionary is fixed. In addition, a multidimensional dictionary learning method that takes advantages of the multidimensional structure is derived, and the influence of sensing matrices is taken into account in the learning process. A joint optimization is achieved via alternately iterating the optimization of the sensing matrix and dictionary. Numerical experiments using both synthetic data and real images demonstrate the superiority of the proposed approaches.

Includes members:
  Member "compressive sensing of sparse tensors":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "rip based performance guarantee for low tubal rank tensor recovery")
  Member "separable cosparse analysis operator learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning co sparse analysis operators with separable structures")
  Member "optimized projections for compressed sensing via rank constrained nearest correlation matrix":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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-4 (induced by source paper "online multilinear dictionary learning")
  Member "simultaneously structured models with application to sparse and low rank matrices":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "joint activity detection and channel estimation for mmw thz wideband massive access")
  Member "compressed sensing of simultaneous low rank and joint sparse matrices":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a dimension reduction based joint activity detection and channel estimation algorithm for massive access")
  Member "simultaneous codeword optimization simco for dictionary update and learning":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "online convex matrix factorization with representative regions")
  Member "iterative hard thresholding for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "efficient active learning of sparse halfspaces with arbitrary bounded noise")

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)