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 "motion corrected multishot mri reconstruction using generative networks with sensitivity encoding"):
  Abstract: Multishot Magnetic Resonance Imaging (MRI) is a promising imaging modality that can produce a high-resolution image with relatively less data acquisition time. The downside of multishot MRI is that it is very sensitive to subject motion and even small amounts of motion during the scan can produce artifacts in the final MR image that may cause misdiagnosis. Numerous efforts have been made to address this issue; however, all of these proposals are limited in terms of how much motion they can correct and the required computational time. In this paper, we propose a novel generative networks based conjugate gradient SENSE (CG-SENSE) reconstruction framework for motion correction in multishot MRI. The proposed framework first employs CG-SENSE reconstruction to produce the motion-corrupted image and then a generative adversarial network (GAN) is used to correct the motion artifacts. The proposed method has been rigorously evaluated on synthetically corrupted data on varying degrees of motion, numbers of shots, and encoding trajectories. Our analyses (both quantitative as well as qualitative/visual analysis) establishes that the proposed method significantly robust and outperforms state-of-the-art motion correction techniques and also reduces severalfold of computational times.

Includes members:
  Member "phonocardiographic sensing using deep learning for abnormal heartbeat detection":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi task semi supervised adversarial autoencoding for speech emotion")
  Member "automating motion correction in multishot mri using generative adversarial networks":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "transfer learning for improving speech emotion classification accuracy":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multi task semi supervised adversarial autoencoding for speech emotion")
  Member "using deep autoencoders for facial expression recognition":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "unsupervised adversarial domain adaptation for cross lingual speech emotion recognition")
  Member "cross corpus speech emotion classification an effective transfer learning technique":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unsupervised adversarial domain adaptation for cross lingual speech emotion recognition")
  Member "generative adversarial networks and adversarial methods in biomedical image analysis":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "red blood cell image generation for data augmentation using conditional generative adversarial networks")
  Member "deep generative adversarial networks for compressed sensing automates mri":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "geometric approaches to increase the expressivity of deep neural networks for mr reconstruction")
  Member "variational autoencoders for learning latent representations of speech emotion a preliminary study":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "multi task semi supervised adversarial autoencoding for speech emotion")

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.CV(Computer Vision and Pattern Recognition)