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: "superimposition of eye fundus images for longitudinal analysis from large public health databases"
  Abstract: In this paper, a method is presented for superimposition (i.e. registration) of eye fundus images from persons with diabetes screened over many years for diabetic retinopathy. The method is fully automatic and robust to camera changes and colour variations across the images both in space and time. All the stages of the process are designed for longitudinal analysis of cohort public health databases where retinal examinations are made at approximately yearly intervals. The method relies on a model correcting two radial distortions and an affine transformation between pairs of images which is robustly fitted on salient points. Each stage involves linear estimators followed by non-linear optimisation. The model of image warping is also invertible for fast computation. The method has been validated (1) on a simulated montage and (2) on public health databases with 69 patients with high quality images (271 pairs acquired mostly with different types of camera and 268 pairs acquired mostly with the same type of camera) with success rates of 92% and 98%, and five patients (20 pairs) with low quality images with a success rate of 100%. Compared to two state-of-the-art methods, ours gives better results.

Connected through hyperedge HE-1 (induced by source paper "region homogeneity in the logarithmic image processing framework application to region growing algorithms"):
  with members:
    - "homogeneity of a region in the logarithmic image processing framework application to region growing algorithms"
    - "double sided probing by map of asplund s distances using logarithmic image processing in the framework of mathematical morphology"
    - "improving shadow suppression for illumination robust face recognition"
    - "spatio colour asplund s metric and logarithmic image processing for colour images lipc"
    - "yfcc100m the new data in multimedia research"

Connected through hyperedge HE-2 (induced by source paper "a link between the multiplicative and additive functional asplund s metrics"):
  with members:
    - "double sided probing by map of asplund s distances using logarithmic image processing in the framework of mathematical morphology"
    - "a simple expression for the map of asplund s distances with the multiplicative logarithmic image processing lip law"
    - "yfcc100m the new data in multimedia research"

Connected through hyperedge HE-3 (induced by source paper "auto classification of retinal diseases in the limit of sparse data using a two streams machine learning model"):
  with members:
    - "a novel hybrid machine learning model for auto classification of retinal diseases"
    - "cardiologist level arrhythmia detection with convolutional neural networks"
    - "chexnet radiologist level pneumonia detection on chest x rays with deep learning"
    - "a novel framework for robustness analysis of visual qa models"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "radnet radiologist level accuracy using deep learning for hemorrhage detection in ct scans"
    - "vqabq visual question answering by basic questions"
    - "robustness analysis of visual qa models by basic questions"

Connected through hyperedge HE-4 (induced by source paper "functional asplund s metrics for pattern matching robust to variable lighting conditions"):
  with members:
    - "logarithmic mathematical morphology a new framework adaptive to illumination changes"
    - "double sided probing by map of asplund s distances using logarithmic image processing in the framework of mathematical morphology"
    - "spatio colour asplund s metric and logarithmic image processing for colour images lipc"
    - "yfcc100m the new data in multimedia research"

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)