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 "neural reparameterization improves structural optimization"):
  Abstract: Structural optimization is a popular method for designing objects such as bridge trusses, airplane wings, and optical devices. Unfortunately, the quality of solutions depends heavily on how the problem is parameterized. In this paper, we propose using the implicit bias over functions induced by neural networks to improve the parameterization of structural optimization. Rather than directly optimizing densities on a grid, we instead optimize the parameters of a neural network which outputs those densities. This reparameterization leads to different and often better solutions. On a selection of 116 structural optimization tasks, our approach produces the best design 50% more often than the best baseline method.

Includes members:
  Member "global optimization of dielectric metasurfaces using a physics driven neural network":
    also connected through other hyperedges:
      (none)
  Member "regularization by architecture a deep prior approach for inverse problems":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "translating diffusion wavelets and regularisation into residual networks")
  Member "3d topology optimization using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "machine learning accelerated topology optimization of nonlinear structures")
  Member "neural networks for topology optimization":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "fea net a physics guided data driven model for efficient mechanical response prediction")
  Member "deep image prior":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "semantic pyramid for image generation")
  Member "multi content gan for few shot font style transfer":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "swaptext image based texts transfer in scenes")
  Member "automated derivation of the adjoint of high level transient finite element programs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "algorithms and software for projections onto intersections of convex and non convex sets with applications to inverse problems")

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)