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: "sparse and low rank decomposition based batch image alignment for speckle reduction of retinal oct images"
  Abstract: Optical Coherence Tomography (OCT) is an emerging technique in the field of biomedical imaging, with applications in ophthalmology, dermatology, coronary imaging etc. Due to the underlying physics, OCT images usually suffer from a granular pattern, called speckle noise, which restricts the process of interpretation. Here, a sparse and low rank decomposition based method is used for speckle reduction in retinal OCT images. This technique works on input data that consists of several B-scans of the same location. The next step is the batch alignment of the images using a sparse and low-rank decomposition based technique. Finally the denoised image is created by median filtering of the low-rank component of the processed data. Simultaneous decomposition and alignment of the images result in better performance in comparison to simple registration-based methods that are used in the literature for noise reduction of OCT images.

Connected through hyperedge HE-1 (induced by source paper "robust principal component analysis for background estimation of particle image velocimetry data"):
  with members:
    - "decomposition into low rank plus additive matrices for background foreground separation a review for a comparative evaluation with a large scale dataset"
    - "robust principal component analysis"

Connected through hyperedge HE-2 (induced by source paper "application of independent component analysis techniques in speckle noise reduction of retinal oct images"):
  with members:
    - "state of the art in retinal optical coherence tomography image analysis"
    - "fast mesh based medical image registration"

Connected through hyperedge HE-3 (induced by source paper "markov random field model based salt and pepper noise removal"):
  with members:
    - "application of independent component analysis techniques in speckle noise reduction of retinal oct images"
    - "structure tensor based image interpolation method"

Connected through hyperedge HE-4 (induced by source paper "finite element method based modeling of cardiac deformation estimation under abnormal ventricular muscle conditions"):
  with members:
    - "application of independent component analysis techniques in speckle noise reduction of retinal oct images"
    - "state of the art in retinal optical coherence tomography image analysis"
    - "a consistent model for cardiac deformation estimation under abnormal ventricular muscle conditions"

Connected through hyperedge HE-5 (induced by source paper "state of the art in retinal optical coherence tomography image analysis"):
  with members:
    - "a panorama on multiscale geometric representations intertwining spatial directional and frequency selectivity"
    - "robust principal component analysis"

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)