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: "convolutional neural networks for non iterative reconstruction of compressively sensed images"
  Abstract: Traditional algorithms for compressive sensing recovery are computationally expensive and are ineffective at low measurement rates. In this work, we propose a data driven non-iterative algorithm to overcome the shortcomings of earlier iterative algorithms. Our solution, ReconNet, is a deep neural network, whose parameters are learned end-to-end to map block-wise compressive measurements of the scene to the desired image blocks. Reconstruction of an image becomes a simple forward pass through the network and can be done in real-time. We show empirically that our algorithm yields reconstructions with higher PSNRs compared to iterative algorithms at low measurement rates and in presence of measurement noise. We also propose a variant of ReconNet which uses adversarial loss in order to further improve reconstruction quality. We discuss how adding a fully connected layer to the existing ReconNet architecture allows for jointly learning the measurement matrix and the reconstruction algorithm in a single network. Experiments on real data obtained from a block compressive imager show that our networks are robust to unseen sensor noise. Finally, through an experiment in object tracking, we show that even at very low measurement rates, reconstructions using our algorithm possess rich semantic content that can be used for high level inference.

Connected through hyperedge HE-1 (induced by source paper "highly scalable image reconstruction using deep neural networks with bandpass filtering"):
  with members:
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "a deep convolutional neural network using directional wavelets for low dose x ray ct reconstruction"
    - "better than real complex valued neural nets for mri fingerprinting"
    - "deep generative adversarial networks for compressed sensing automates mri"
    - "deep complex networks"
    - "deep artifact learning for compressed sensing and parallel mri"
    - "learning a variational network for reconstruction of accelerated mri data"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-2 (induced by source paper "compressively sensed image recognition"):
  with members:
    - "compressed learning a deep neural network approach"
    - "reconstruction free action inference from compressive imagers"
    - "from denoising to compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "global guarantees for enforcing deep generative priors by empirical risk"):
  with members:
    - "how generative adversarial networks and their variants work an overview"
    - "towards moderate overparameterization global convergence guarantees for training shallow neural networks"
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "learning overparameterized neural networks via stochastic gradient descent on structured data"
    - "phase retrieval under a generative prior"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "a convergence theory for deep learning via over parameterization"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"

Connected through hyperedge HE-4 (induced by source paper "deep learning for massive mimo csi feedback"):
  with members:
    - "dr2 net deep residual reconstruction network for image compressive sensing"
    - "deep learning for wireless physical layer opportunities and challenges"
    - "deep learning based mimo communications"
    - "deep residual learning for image recognition"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "from denoising to compressed sensing"
    - "downlink training techniques for fdd massive mimo systems open loop and closed loop training with memory"
    - "on capacity of large scale mimo multiple access channels with distributed sets of correlated antennas"

Connected through hyperedge HE-5 (induced by source paper "fully convolutional measurement network for compressive sensing image reconstruction"):
  with members:
    - "dr2 net deep residual reconstruction network for image compressive sensing"
    - "enhanced deep residual networks for single image super resolution"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "a deep learning approach to structured signal recovery"
    - "deep residual learning for image recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "model based compressive sensing"
    - "message passing algorithms for compressed sensing"

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)