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: "better than real complex valued neural nets for mri fingerprinting"
  Abstract: The task of MRI fingerprinting is to identify tissue parameters from complex-valued MRI signals. The prevalent approach is dictionary based, where a test MRI signal is compared to stored MRI signals with known tissue parameters and the most similar signals and tissue parameters retrieved. Such an approach does not scale with the number of parameters and is rather slow when the tissue parameter space is large. #R##N#Our first novel contribution is to use deep learning as an efficient nonlinear inverse mapping approach. We generate synthetic (tissue, MRI) data from an MRI simulator, and use them to train a deep net to map the MRI signal to the tissue parameters directly. #R##N#Our second novel contribution is to develop a complex-valued neural network with new cardioid activation functions. Our results demonstrate that complex-valued neural nets could be much more accurate than real-valued neural nets at complex-valued MRI fingerprinting.

Connected through hyperedge HE-1 (induced by source paper "compressive mri quantification using convex spatiotemporal priors and deep auto encoders"):
  with members:
    - "deep mr fingerprinting with total variation and low rank subspace priors"
    - "magnetic resonance fingerprinting reconstruction via spatiotemporal convolutional neural networks"
    - "magnetic resonance fingerprinting using recurrent neural networks"
    - "coverblip accelerated and scalable iterative matched filtering for magnetic resonance fingerprint reconstruction"
    - "geometry of deep learning for magnetic resonance fingerprinting"
    - "mr fingerprinting deep reconstruction network drone"
    - "deep kernel learning"
    - "very deep convolutional networks for large scale image recognition"

Connected through hyperedge HE-2 (induced by source paper "deep mr fingerprinting with total variation and low rank subspace priors"):
  with members:
    - "magnetic resonance fingerprinting reconstruction via spatiotemporal convolutional neural networks"
    - "coverblip accelerated and scalable iterative matched filtering for magnetic resonance fingerprint reconstruction"
    - "geometry of deep learning for magnetic resonance fingerprinting"

Connected through hyperedge HE-3 (induced by source paper "fourier rnns for sequence prediction"):
  with members:
    - "neural decomposition of time series data for effective generalization"
    - "complex gated recurrent neural networks"
    - "gated orthogonal recurrent units on learning to forget"
    - "deep complex networks"
    - "multiresolution recurrent neural networks an application to dialogue response generation"
    - "hierarchical multiscale recurrent neural networks"
    - "comparing time and frequency domain for audio event recognition using deep learning"
    - "unitary evolution recurrent neural networks"

Connected through hyperedge HE-4 (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"
    - "deep generative adversarial networks for compressed sensing automates mri"
    - "deep complex networks"
    - "deep artifact learning for compressed sensing and parallel mri"
    - "convolutional neural networks for non iterative reconstruction of compressively sensed images"
    - "learning a variational network for reconstruction of accelerated mri data"
    - "tensorflow large scale machine learning on heterogeneous distributed systems"

Connected through hyperedge HE-5 (induced by source paper "complex valued neural networks with non parametric activation functions"):
  with members:
    - "self normalizing neural networks"
    - "deep complex networks"
    - "swish a self gated activation function"
    - "associative long short term memory"
    - "on complex valued convolutional neural networks"
    - "unitary evolution recurrent neural networks"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "learning activation functions to improve deep neural networks"

Connected through hyperedge HE-6 (induced by source paper "coverblip accelerated and scalable iterative matched filtering for magnetic resonance fingerprint reconstruction"):
  with members:
    - "inexact gradient projection and fast data driven compressed sensing"
    - "mr fingerprinting deep reconstruction network drone"
    - "compressive source separation theory and methods for hyperspectral imaging"
    - "a compressed sensing framework for magnetic resonance fingerprinting"
    - "sparse subspace clustering algorithm theory and applications"
    - "model based compressive sensing"
    - "sampling and reconstructing signals from a union of linear subspaces"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "geometry of deep learning for magnetic resonance fingerprinting"):
  with members:
    - "magnetic resonance fingerprinting reconstruction via spatiotemporal convolutional neural networks"
    - "coverblip accelerated and scalable iterative matched filtering for magnetic resonance fingerprint reconstruction"
    - "inexact gradient projection and fast data driven compressed sensing"
    - "geometric deep learning going beyond euclidean data"
    - "mr fingerprinting deep reconstruction network drone"
    - "efficient representation of low dimensional manifolds using deep networks"
    - "adam a method for stochastic optimization"
    - "a compressed sensing framework for magnetic resonance fingerprinting"

Connected through hyperedge HE-8 (induced by source paper "magnetic resonance fingerprinting using recurrent neural networks"):
  with members:
    - "magnetic resonance fingerprinting reconstruction via spatiotemporal convolutional neural networks"
    - "geometry of deep learning for magnetic resonance fingerprinting"
    - "comparative study of cnn and rnn for natural language processing"
    - "mr fingerprinting deep reconstruction network drone"
    - "on the properties of neural machine translation encoder decoder approaches"
    - "mrf denoising with compressed sensing and adaptive filtering"

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)