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 "theoretical interpretation of learned step size in deep unfolded gradient descent"):
  Abstract: Deep unfolding is a promising deep-learning technique in which an iterative algorithm is unrolled to a deep network architecture with trainable parameters. In the case of gradient descent algorithms, as a result of the training process, one often observes the acceleration of the convergence speed with learned non-constant step size parameters whose behavior is not intuitive nor interpretable from conventional theory. In this paper, we provide a theoretical interpretation of the learned step size of deep-unfolded gradient descent (DUGD). We first prove that the training process of DUGD reduces not only the mean squared error loss but also the spectral radius related to the convergence rate. Next, we show that minimizing the upper bound of the spectral radius naturally leads to the Chebyshev step which is a sequence of the step size based on Chebyshev polynomials. The numerical experiments confirm that the Chebyshev steps qualitatively reproduce the learned step size parameters in DUGD, which provides a plausible interpretation of the learned parameters. Additionally, we show that the Chebyshev steps achieve the lower bound of the convergence rate for the first-order method in a specific limit without learning parameters or momentum terms.

Includes members:
  Member "chebyshev inertial landweber algorithm for linear inverse problems":
    also connected through other hyperedges:
      (none)
  Member "degrees of freedom analysis of unrolled neural networks":
    also connected through other hyperedges:
      (none)
  Member "trainable ista for sparse signal recovery":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "model driven deep learning for joint mimo channel estimation and signal detection")
  Member "deep learning aided trainable projected gradient decoding for ldpc codes":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "complex field trainable ista for linear and nonlinear inverse problems")
  Member "neural proximal gradient descent for compressive imaging":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "neumann networks for inverse problems in imaging")
  Member "theoretical linear convergence of unfolded ista and its practical weights and thresholds":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a novel learnable gradient descent type algorithm for non convex non smooth inverse problems")
  Member "trainable projected gradient detector for massive overloaded mimo channels data driven tuning approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep learning for csi feedback based on superimposed coding")
  Member "a model driven deep learning network for mimo detection":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a deep learning framework for optimization of miso downlink beamforming")

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)