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 "compressed sensing with probability based prior information"):
  Abstract: This paper deals with the design of a sensing matrix along with a sparse recovery algorithm by utilizing the probability-based prior information for compressed sensing system. With the knowledge of the probability for each atom of the dictionary being used, a diagonal weighted matrix is obtained and then the sensing matrix is designed by minimizing a weighted function such that the Gram of the equivalent dictionary is as close to the Gram of dictionary as possible. An analytical solution for the corresponding sensing matrix is derived which leads to low computational complexity. We also exploit this prior information through the sparse recovery stage and propose a probability-driven orthogonal matching pursuit algorithm that improves the accuracy of the recovery. Simulations for synthetic data and application scenarios of surveillance video are carried out to compare the performance of the proposed methods with some existing algorithms. The results reveal that the proposed CS system outperforms existing CS systems.

Includes members:
  Member "subspace orbit randomized decomposition for low rank matrix approximations":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "study of compressed randomized utv decompositions for low rank matrix approximations in data science")
  Member "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "medgan medical image translation using gans")
  Member "masked signal decomposition using subspace representation and its applications":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "masked rpca sparse and low rank decomposition under overlaying model and application to moving object detection")
  Member "on collaborative compressive sensing systems the framework design and algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "online learning sensing matrix and sparsifying dictionary simultaneously for compressive sensing")
  Member "widely linear precoding for large scale mimo with iqi algorithms and performance analysis":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "study of coarse quantization aware block diagonalization algorithms for mimo systems with low resolution")
  Member "joint sensing matrix and sparsifying dictionary optimization for tensor compressive sensing":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "online multilinear dictionary learning")
  Member "iterative detection and decoding algorithms for mimo systems in block fading channels using ldpc codes":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "study of coarse quantization aware block diagonalization algorithms for mimo systems with low resolution")
  Member "robust adaptive beamforming based on low rank and cross correlation techniques":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "study of coarse quantization aware block diagonalization algorithms for mimo systems with low resolution")

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)