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: "estimation of fiber orientations using neighborhood information"
  Abstract: Abstract   Data from  diffusion magnetic resonance imaging  (dMRI) can be used to reconstruct fiber tracts, for example, in muscle and white matter. Estimation of  fiber orientations  (FOs) is a crucial step in the reconstruction process and these estimates can be corrupted by noise. In this paper, a new method called  Fiber Orientation Reconstruction using Neighborhood Information  (FORNI) is described and shown to reduce the effects of noise and improve FO estimation performance by incorporating spatial consistency. FORNI uses a fixed tensor basis to model the diffusion weighted signals, which has the advantage of providing an explicit relationship between the basis vectors and the FOs. FO spatial coherence is encouraged using weighted l 1 -norm regularization terms, which contain the interaction of directional information between neighbor voxels. Data fidelity is encouraged using a squared error between the observed and reconstructed diffusion weighted signals. After appropriate weighting of these competing objectives, the resulting objective function is minimized using a block coordinate descent algorithm, and a straightforward parallelization strategy is used to speed up processing. Experiments were performed on a digital crossing phantom,  ex vivo  tongue dMRI data, and  in vivo #N#brain dMRI data for both qualitative and quantitative evaluation. The results demonstrate that FORNI improves the quality of FO estimation over other state of the art algorithms.

Connected through hyperedge HE-1 (induced by source paper "fiber orientation estimation guided by a deep network"):
  with members:
    - "maximal sparsity with deep networks"
    - "learning a convolutional neural network for non uniform motion blur removal"
    - "adam a method for stochastic optimization"
    - "going deeper with convolutions"
    - "learning efficient sparse and low rank models"

Connected through hyperedge HE-2 (induced by source paper "estimation of tissue microstructure using a deep network inspired by a sparse reconstruction framework"):
  with members:
    - "maximal sparsity with deep networks"
    - "adam a method for stochastic optimization"
    - "learning efficient sparse and low rank models"

Connected through hyperedge HE-3 (induced by source paper "learning based ensemble average propagator estimation"):
  with members:
    - "adam a method for stochastic optimization"

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)