human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "a recursive born approach to nonlinear inverse scattering"
  Abstract: The iterative Born approximation (IBA) is a well-known method for describing waves scattered by semitransparent objects. In this letter, we present a novel nonlinear inverse scattering method that combines IBA with an edge-preserving total variation regularizer. The proposed method is obtained by relating iterations of IBA to layers of an artificial multilayer neural network and developing a corresponding error backpropagation algorithm for efficiently estimating the permittivity of the object. Simulations illustrate that, by accounting for multiple scattering, the method successfully recovers the permittivity distribution where the traditional linear inverse scattering fails.

Connected through hyperedge HE-1 (induced by source paper "stability of scattering decoder for nonlinear diffractive imaging"):
  with members:
    - "efficient inversion of multiple scattering model for optical diffraction tomography"
    - "deep convolutional neural network for inverse problems in imaging"
    - "seagle sparsity driven image reconstruction under multiple scattering"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-2 (induced by source paper "efficient inversion of multiple scattering model for optical diffraction tomography"):
  with members:
    - "seagle sparsity driven image reconstruction under multiple scattering"
    - "compressive imaging with iterative forward models"

Connected through hyperedge HE-3 (induced by source paper "accelerated image reconstruction for nonlinear diffractive imaging"):
  with members:
    - "seagle sparsity driven image reconstruction under multiple scattering"
    - "compressive imaging with iterative forward models"

Connected through hyperedge HE-4 (induced by source paper "seagle sparsity driven image reconstruction under multiple scattering"):
  with members:
    - "compressive imaging with iterative forward models"
    - "sparse modeling for image and vision processing"

Question: Please tell me which class the center node 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)