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: "image fusion using lep filtering and bilinear interpolation"
  Abstract: Image Fusion is the process in which core information from a set of component images is merged to form a single image, which is more informative and complete than the component input images in quality and appearance. This paper presents a fast and effective image fusion method for creating high quality fused images by merging component images. In the proposed method, the input image is broken down to a two-scale image representation with a base layer having large scale variations in intensity, and a detail layer containing small scale details. Here fusion of the base and detail layers is implemented by means of a Local Edge preserving filtering based technique. The proposed method is an efficient image fusion technique in which the noise component is very low and quality of the resultant image is high so that it can be used for applications like medical image processing, requiring very accurate edge preserved images. Performance is tested by calculating PSNR and SSIM of images. The benefit of the proposed method is that it removes noise without altering the underlying structures of the image. This paper also presents an image zooming technique using bilinear interpolation in which a portion of the input image is cropped and bilinear interpolation is applied. Experimental results showed that the when PSNR value is calculated, the noise is found to be very low for the resultant image portion.

Connected through hyperedge HE-1 (induced by source paper "deep seismic prior based reconstruction of seismic data using convolutional neural networks"):
  with members:
    - "interpolation and denoising of seismic data using convolutional neural networks"
    - "shift net image inpainting via deep feature rearrangement"
    - "image restoration using convolutional auto encoders with symmetric skip connections"
    - "u net convolutional networks for biomedical image segmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"
    - "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)