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 "soft autoencoder and its wavelet shrinkage interpretation"):
  Abstract: Deep learning is a main focus of artificial intelligence and has greatly impacted other fields. However, deep learning is often criticized for its lack of interpretation. As a successful unsupervised model in deep learning, various autoencoders, especially convolutional autoencoders, are very popular and important. Since these autoencoders need improvements and insights, in this paper we shed light on the nonlinearity of a deep convolutional autoencoder in perspective of perfect signal recovery. In particular, we propose a new type of convolutional autoencoders, termed as Soft-Autoencoder (Soft-AE), in which the activations of encoding layers are implemented with adaptable soft-thresholding units while decoding layers are realized with linear units. Consequently, Soft-AE can be naturally interpreted as a learned cascaded wavelet shrinkage system. Our denoising numerical experiments on CIFAR-10, BSD-300 and Mayo Clinical Challenge Dataset demonstrate that Soft-AE gives a competitive performance relative to its counterparts.

Includes members:
  Member "competitive performance of a modularized deep neural network compared to commercial algorithms for low dose ct image reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "shape and margin aware lung nodule classification in low dose ct images via soft activation mapping")
  Member "exact and consistent interpretation for piecewise linear neural networks a closed form solution":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "analytic marching an analytic meshing solution from deep implicit surface networks")
  Member "fuzzy logic interpretation of artificial neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "quadratic autoencoder q ae for low dose ct denoising")
  Member "3 d convolutional encoder decoder network for low dose ct via transfer learning from a 2 d trained network":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "boundary weighted domain adaptive neural network for prostate mr image segmentation")
  Member "fastmri an open dataset and benchmarks for accelerated mri":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "geometric approaches to increase the expressivity of deep neural networks for mr reconstruction")
  Member "a new type of neurons for machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "quadratic autoencoder q ae for low dose ct denoising")
  Member "revisit fuzzy neural network demystifying batch normalization and relu with generalized hamming network":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "optimize tsk fuzzy systems for classification problems mini batch gradient descent with uniform regularization and batch normalization")
  Member "a deep convolutional auto encoder with pooling unpooling layers in caffe":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "thumbnet one thumbnail image contains all you need for recognition")

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)