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: "computer aided detection of polyps in optical colonoscopy images"
  Abstract: We present a computer-aided detection algorithm for polyps in optical colonoscopy images. Polyps are the precursors to colon cancer. In the US alone, 14 million optical colonoscopies are performed every year, mostly to screen for polyps. Optical colonoscopy has been shown to have an approximately 25% polyp miss rate due to the convoluted folds and bends present in the colon. In this work, we present an automatic detection algorithm to detect these polyps in the optical colonoscopy images. We use a machine learning algorithm to infer a depth map for a given optical colonoscopy image and then use a detailed pre-built polyp profile to detect and delineate the boundaries of polyps in this given image. We have achieved the best recall of 84.0% and the best specificity value of 83.4%.

Connected through hyperedge HE-1 (induced by source paper "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"):
  with members:
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "gans for biological image synthesis"
    - "deep generative adversarial networks for compressed sensing automates mri"
    - "unsupervised histopathology image synthesis"
    - "depthsynth real time realistic synthetic data generation from cad models for 2 5d recognition"
    - "learning from synthetic humans"
    - "deep learning and conditional random fields based depth estimation and topographical reconstruction from conventional endoscopy"
    - "adversarial training and dilated convolutions for brain mri segmentation"

Connected through hyperedge HE-2 (induced by source paper "deep learning with cinematic rendering fine tuning deep neural networks using photorealistic medical images"):
  with members:
    - "generative adversarial networks an overview"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "exploring generalization in deep learning"
    - "building disease detection algorithms with very small numbers of positive samples"
    - "fine tuning deep cnn models on specific ms coco categories"
    - "depthsynth real time realistic synthetic data generation from cad models for 2 5d recognition"

Connected through hyperedge HE-3 (induced by source paper "sparse then dense alignment based 3d map reconstruction method for endoscopic capsule robots"):
  with members:
    - "deep endovo a recurrent convolutional neural network rcnn based visual odometry approach for endoscopic capsule robots"
    - "a fully dense and globally consistent 3d map reconstruction approach for gi tract to enhance therapeutic relevance of the endoscopic capsule robot"
    - "a non rigid map fusion based direct slam method for endoscopic capsule robots"
    - "six degree of freedom localization of endoscopic capsule robots using recurrent neural networks embedded into a convolutional neural network"
    - "a deep learning based 6 degree of freedom localization method for endoscopic capsule robots"

Connected through hyperedge HE-4 (induced by source paper "deep learning and conditional random fields based depth estimation and topographical reconstruction from conventional endoscopy"):
  with members:
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "understanding the mechanisms of deep transfer learning for medical images"
    - "multi scale continuous crfs as sequential deep networks for monocular depth estimation"
    - "a survey on deep learning in medical image analysis"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "polyp detection and segmentation from video capsule endoscopy a review"
    - "learning depth from single monocular images using deep convolutional neural fields"

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)