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 "tradeoffs between convergence speed and reconstruction accuracy in inverse problems"):
  Abstract: Solving inverse problems with iterative algorithms is popular, especially for large data. Due to time constraints, the number of possible iterations is usually limited, potentially affecting the achievable accuracy. Given an error one is willing to tolerate, an important question is whether it is possible to modify the original iterations to obtain faster convergence to a minimizer achieving the allowed error without increasing the computational cost of each iteration considerably. Relying on recent recovery techniques developed for settings in which the desired signal belongs to some low-dimensional set, we show that using a coarse estimate of this set may lead to faster convergence at the cost of an additional reconstruction error related to the accuracy of the set approximation. Our theory ties to recent advances in sparse recovery, compressed sensing, and deep learning. Particularly, it may provide a possible explanation to the successful approximation of the l1-minimization solution by neural networks with layers representing iterations, as practiced in the learned iterative shrinkage-thresholding algorithm (LISTA).

Includes members:
  Member "learning to learn by gradient descent by gradient descent":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")
  Member "maximal sparsity with deep networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "theoretical interpretation of learned step size in deep unfolded gradient descent")
  Member "onsager corrected deep learning for sparse linear inverse problems":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "learning to detect")
  Member "accelerating eulerian fluid simulation with convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "fea net a physics guided data driven model for efficient mechanical response prediction")
  Member "sampling stationary signals subject to bitrate constraints":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "oblivious processing in a fronthaul constrained gaussian channel")
  Member "raisr rapid and accurate image super resolution":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "super resolution via image adapted denoising cnns incorporating external and internal learning")
  Member "fundamental distortion limits of analog to digital compression":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gaussian approximation of quantization error for estimation from compressed data")
  Member "a picture is worth a billion bits real time image reconstruction from dense binary pixels":
    also connected through other hyperedges:
      (none)

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.NA(Numerical Analysis)