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 denoising via cnns an adversarial approach"
  Abstract: Is it possible to recover an image from its noisy version using convolutional neural networks? This is an interesting problem as convolutional layers are generally used as feature detectors for tasks like classification, segmentation and object detection. We present a new CNN architecture for blind image denoising which synergically combines three architecture components, a multi-scale feature extraction layer which helps in reducing the effect of noise on feature maps, an l_p regularizer which helps in selecting only the appropriate feature maps for the task of reconstruction, and finally a three step training approach which leverages adversarial training to give the final performance boost to the model. The proposed model shows competitive denoising performance when compared to the state-of-the-art approaches.

Connected through hyperedge HE-1 (induced by source paper "adversarially learned one class classifier for novelty detection"):
  with members:
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "abnormal event detection in videos using generative adversarial nets"
    - "coherence pursuit fast simple and robust principal component analysis"
    - "training adversarial discriminators for cross channel abnormal event detection in crowds"
    - "improved techniques for training gans"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"

Connected through hyperedge HE-2 (induced by source paper "avid adversarial visual irregularity detection"):
  with members:
    - "a self training method for semi supervised gans"
    - "adversarially learned one class classifier for novelty detection"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "abnormal event detection in videos using generative adversarial nets"
    - "approximation and convergence properties of generative adversarial learning"
    - "provable self representation based outlier detection in a union of subspaces"
    - "future frame prediction for anomaly detection a new baseline"
    - "training adversarial discriminators for cross channel abnormal event detection in crowds"

Connected through hyperedge HE-3 (induced by source paper "correction by projection denoising images with generative adversarial networks"):
  with members:
    - "ffdnet toward a fast and flexible solution for cnn based image denoising"
    - "inverting the generator of a generative adversarial network ii"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "unrolled generative adversarial networks"
    - "adversarial feature learning"
    - "unsupervised representation learning with deep convolutional generative adversarial networks"
    - "understanding deep image representations by inverting them"
    - "deep learning face attributes in the wild"

Connected through hyperedge HE-4 (induced by source paper "deepfuse a deep unsupervised approach for exposure fusion with extreme exposure image pairs"):
  with members:
    - "mask r cnn"
    - "enabling my robot to play pictionary recurrent neural networks for sketch recognition"
    - "r fcn object detection via region based fully convolutional networks"
    - "vqa visual question answering"
    - "deep residual learning for image recognition"
    - "loss functions for neural networks for image processing"
    - "recurrent convolutional neural networks for scene parsing"

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)