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: "coverblip accelerated and scalable iterative matched filtering for magnetic resonance fingerprint reconstruction"
  Abstract: Current popular methods for Magnetic Resonance Fingerprint (MRF) recovery are bottlenecked by the heavy computations of a matched-filtering step due to the growing size and complexity of the fingerprint dictionaries in multi-parametric quantitative MRI applications. We address this shortcoming by arranging dictionary atoms in the form of cover tree structures and adopt the corresponding fast approximate nearest neighbour searches to accelerate matched-filtering. For datasets belonging to smooth low-dimensional manifolds cover trees offer search complexities logarithmic in terms of data population. With this motivation we propose an iterative reconstruction algorithm, named CoverBLIP, to address large-size MRF problems where the fingerprint dictionary i.e. discrete manifold of Bloch responses, encodes several intrinsic NMR parameters. We study different forms of convergence for this algorithm and we show that provided with a notion of embedding, the inexact and non-convex iterations of CoverBLIP linearly convergence toward a near-global solution with the same order of accuracy as using exact brute-force searches. Our further examinations on both synthetic and real-world datasets and using different sampling strategies, indicates between 2 to 3 orders of magnitude reduction in total search computations. Cover trees are robust against the curse-of-dimensionality and therefore CoverBLIP provides a notion of scalability -- a consistent gain in time-accuracy performance-- for searching high-dimensional atoms which may not be easily preprocessed (i.e. for dimensionality reduction) due to the increasing degrees of non-linearities appearing in the emerging multi-parametric MRF dictionaries.

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"
    - "geometry of deep learning for magnetic resonance fingerprinting"
    - "better than real complex valued neural nets for mri 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"
    - "geometry of deep learning for magnetic resonance fingerprinting"
    - "better than real complex valued neural nets for mri fingerprinting"

Connected through hyperedge HE-3 (induced by source paper "geometry of deep learning for magnetic resonance fingerprinting"):
  with members:
    - "magnetic resonance fingerprinting reconstruction via spatiotemporal convolutional neural networks"
    - "better than real complex valued neural nets for mri fingerprinting"
    - "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"

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)