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 "highly scalable image reconstruction using deep neural networks with bandpass filtering"):
  Abstract: To increase the flexibility and scalability of deep neural networks for image reconstruction, a framework is proposed based on bandpass filtering. For many applications, sensing measurements are performed indirectly. For example, in magnetic resonance imaging, data are sampled in the frequency domain. The introduction of bandpass filtering enables leveraging known imaging physics while ensuring that the final reconstruction is consistent with actual measurements to maintain reconstruction accuracy. We demonstrate this flexible architecture for reconstructing subsampled datasets of MRI scans. The resulting high subsampling rates increase the speed of MRI acquisitions and enable the visualization rapid hemodynamics.

Includes members:
  Member "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "medgan medical image translation using gans")
  Member "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "learning when and where to zoom with deep reinforcement learning")
  Member "better than real complex valued neural nets for mri fingerprinting":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "compressive mri quantification using convex spatiotemporal priors and deep auto encoders")
  Member "deep generative adversarial networks for compressed sensing automates mri":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "geometric approaches to increase the expressivity of deep neural networks for mr reconstruction")
  Member "deep complex networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "deep quaternion features for privacy protection")
  Member "deep artifact learning for compressed sensing and parallel mri":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "motion artifact reduction using a convolutional neural network for dynamic contrast enhanced mr imaging of the liver")
  Member "convolutional neural networks for non iterative reconstruction of compressively sensed images":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "compressively sensed image recognition")
  Member "learning a variational network for reconstruction of accelerated mri data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "geometric approaches to increase the expressivity of deep neural networks for mr reconstruction")

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)