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: "a review paper noise models in digital image processing"
  Abstract: Noise is always presents in digital images during image acquisition, coding, transmission, and processing steps. Noise is very difficult to remove it from the digital images without the prior knowledge of noise model. That is why, review of noise models are essential in the study of image denoising techniques. In this paper, we express a brief overview of various noise models. These noise models can be selected by analysis of their origin. In this way, we present a complete and quantitative analysis of noise models available in digital images.

Connected through hyperedge HE-1 (induced by source paper "ai powered gui attack and its defensive methods"):
  with members:
    - "black box adversarial attacks with bayesian optimization"
    - "adversarial examples versus cloud based detectors a black box empirical study"
    - "using ai to hack ia a new stealthy spyware against voice assistance functions in smart phones"
    - "the elephant in the room"
    - "transferability in machine learning from phenomena to black box attacks using adversarial samples"
    - "distillation as a defense to adversarial perturbations against deep neural networks"

Connected through hyperedge HE-2 (induced by source paper "sketch2code generating a website from a paper mockup"):
  with members:
    - "machine learning based prototyping of graphical user interfaces for mobile apps"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "pix2code generating code from a graphical user interface screenshot"
    - "richer convolutional features for edge detection"
    - "deep direct regression for multi oriented scene text detection"
    - "r2cnn rotational region cnn for orientation robust scene text detection"
    - "a review on deep learning techniques applied to semantic segmentation"

Connected through hyperedge HE-3 (induced by source paper "ls net fast single shot line segment detector"):
  with members:
    - "group normalization"
    - "wing loss for robust facial landmark localisation with convolutional neural networks"
    - "focal loss for dense object detection"
    - "richer convolutional features for edge detection"
    - "ssd single shot multibox detector"
    - "understanding data augmentation for classification when to warp"
    - "r fcn object detection via region based fully convolutional networks"
    - "tensorflow a system for large scale machine learning"

Connected through hyperedge HE-4 (induced by source paper "fast bayesian restoration of poisson corrupted images with inla"):
  with members:
    - "bayesian image restoration for poisson corrupted image using a latent variational method with gaussian mrf"

Connected through hyperedge HE-5 (induced by source paper "improving robustness of feature representations to image deformations using powered convolution in cnns"):
  with members:
    - "an empirical study on the effects of different types of noise in image classification tasks"
    - "understanding how image quality affects deep neural networks"

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)