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: "structurally sensitive multi scale deep neural network for low dose ct denoising"
  Abstract: Computed tomography (CT) is a popular medical imaging modality and enjoys wide clinical applications. At the same time, the X-ray radiation dose associated with CT scannings raises a public concern due to its potential risks to the patients. Over the past years, major efforts have been dedicated to the development of low-dose CT (LDCT) methods. However, the radiation dose reduction compromises the signal-to-noise ratio, leading to strong noise and artifacts that down-grade the CT image quality. In this paper, we propose a novel 3-D noise reduction method, called structurally sensitive multi-scale generative adversarial net, to improve the LDCT image quality. Specifically, we incorporate 3-D volumetric information to improve the image quality. Also, different loss functions for training denoising models are investigated. Experiments show that the proposed method can effectively preserve the structural and textural information in reference to the normal-dose CT images and significantly suppress noise and artifacts. Qualitative visual assessments by three experienced radiologists demonstrate that the proposed method retrieves more information and outperforms competing methods.

Connected through hyperedge HE-1 (induced by source paper "v2i connectivity based dynamic queue jumper lane for emergency vehicles an approximate dynamic programming approach"):
  with members:
    - "combining deep reinforcement learning and safety based control for autonomous driving"
    - "prioritized experience replay"

Connected through hyperedge HE-2 (induced by source paper "quadratic autoencoder q ae for low dose ct denoising"):
  with members:
    - "competitive performance of a modularized deep neural network compared to commercial algorithms for low dose ct image reconstruction"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "universal approximation with quadratic deep networks"
    - "fuzzy logic interpretation of artificial neural networks"
    - "dense associative memory is robust to adversarial inputs"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "non linear convolution filters for cnn based learning"
    - "a new type of neurons for machine learning"

Connected through hyperedge HE-3 (induced by source paper "competitive performance of a modularized deep neural network compared to commercial algorithms for low dose ct image reconstruction"):
  with members:
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "sharpness aware low dose ct denoising using conditional generative adversarial network"
    - "image reconstruction by domain transform manifold learning"
    - "3 d convolutional encoder decoder network for low dose ct via transfer learning from a 2 d trained network"
    - "improved training of wasserstein gans"
    - "u net convolutional networks for biomedical image segmentation"
    - "on the difficulty of training recurrent neural networks"

Connected through hyperedge HE-4 (induced by source paper "fuzzy logic interpretation of artificial neural networks"):
  with members:
    - "geometric understanding of deep learning"
    - "exact and consistent interpretation for piecewise linear neural networks a closed form solution"
    - "a new type of neurons for machine learning"
    - "interpreting deep visual representations via network dissection"
    - "revisit fuzzy neural network demystifying batch normalization and relu with generalized hamming network"
    - "a geometric view of optimal transportation and generative model"
    - "visualizing the loss landscape of neural nets"
    - "sharp minima can generalize for deep nets"

Connected through hyperedge HE-5 (induced by source paper "fuzzy logic interpretation of quadratic networks"):
  with members:
    - "geometric understanding of deep learning"
    - "exact and consistent interpretation for piecewise linear neural networks a closed form solution"
    - "an empirical evaluation of rule extraction from recurrent neural networks"
    - "a new type of neurons for machine learning"
    - "interpreting deep visual representations via network dissection"
    - "revisit fuzzy neural network demystifying batch normalization and relu with generalized hamming network"
    - "sharp minima can generalize for deep nets"
    - "the loss surface of deep and wide neural networks"

Connected through hyperedge HE-6 (induced by source paper "low dose ct via deep cnn with skip connection and network in network"):
  with members:
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "network in network"

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.CV(Computer Vision and Pattern Recognition)