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: "exploiting the potential of unlabeled endoscopic video data with self supervised learning"
  Abstract: Surgical data science is a new research field that aims to observe all aspects of the patient treatment process in order to provide the right assistance at the right time. Due to the breakthrough successes of deep learning-based solutions for automatic image annotation, the availability of reference annotations for algorithm training is becoming a major bottleneck in the field. The purpose of this paper was to investigate the concept of self-supervised learning to address this issue. #R##N#Our approach is guided by the hypothesis that unlabeled video data can be used to learn a representation of the target domain that boosts the performance of state-of-the-art machine learning algorithms when used for pre-training. Core of the method is an auxiliary task based on raw endoscopic video data of the target domain that is used to initialize the convolutional neural network (CNN) for the target task. In this paper, we propose the re-colorization of medical images with a generative adversarial network (GAN)-based architecture as auxiliary task. A variant of the method involves a second pre-training step based on labeled data for the target task from a related domain. We validate both variants using medical instrument segmentation as target task. #R##N#The proposed approach can be used to radically reduce the manual annotation effort involved in training CNNs. Compared to the baseline approach of generating annotated data from scratch, our method decreases exploratively the number of labeled images by up to 75% without sacrificing performance. Our method also outperforms alternative methods for CNN pre-training, such as pre-training on publicly available non-medical or medical data using the target task (in this instance: segmentation). #R##N#As it makes efficient use of available (non-)public and (un-)labeled data, the approach has the potential to become a valuable tool for CNN (pre-)training.

Connected through hyperedge HE-1 (induced by source paper "3d medical image segmentation with labeled and unlabeled data using autoencoders at the example of liver segmentation in ct images"):
  with members:
    - "cats or cat scans transfer learning from natural or medical image source data sets"
    - "self supervised learning for cardiac mr image segmentation by anatomical position prediction"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "nnu net breaking the spell on successful medical image segmentation"
    - "surrogate supervision for medical image analysis effective deep learning from limited quantities of labeled data"
    - "model evaluation model selection and algorithm selection in machine learning"
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "nnu net self adapting framework for u net based medical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "surrogate supervision for medical image analysis effective deep learning from limited quantities of labeled data"):
  with members:
    - "automatic segmentation of pulmonary lobes using a progressive dense v network"
    - "unsupervised representation learning by predicting image rotations"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "network dissection quantifying interpretability of deep visual representations"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "progressive growing of gans for improved quality stability and variation"
    - "colorization as a proxy task for visual understanding"
    - "accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks"

Connected through hyperedge HE-3 (induced by source paper "self supervised learning for cardiac mr image segmentation by anatomical position prediction"):
  with members:
    - "surrogate supervision for medical image analysis effective deep learning from limited quantities of labeled data"
    - "unsupervised representation learning by predicting image rotations"
    - "automated cardiovascular magnetic resonance image analysis with fully convolutional networks"
    - "multi task self supervised visual learning"
    - "self supervised learning for spinal mris"
    - "context encoders feature learning by inpainting"
    - "colorful image colorization"
    - "unsupervised visual representation learning by context prediction"

Connected through hyperedge HE-4 (induced by source paper "self supervised surgical tool segmentation using kinematic information"):
  with members:
    - "weakly supervised learning for tool localization in laparoscopic videos"
    - "time contrastive networks self supervised learning from video"
    - "concurrent segmentation and localization for tracking of surgical instruments"
    - "deep residual learning for instrument segmentation in robotic surgery"
    - "toolnet holistically nested real time segmentation of robotic surgical tools"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "fully convolutional networks for semantic segmentation"

Connected through hyperedge HE-5 (induced by source paper "semi supervised medical image segmentation via learning consistency under transformations"):
  with members:
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "semi supervised skin lesion segmentation via transformation consistent self ensembling model"
    - "improving the segmentation of anatomical structures in chest radiographs using u net with an imagenet pre trained encoder"
    - "deep semi supervised segmentation with weight averaged consistency targets"
    - "semi supervised deep learning for fully convolutional networks"
    - "improving landmark localization with semi supervised learning"
    - "regularization with stochastic transformations and perturbations for deep semi supervised learning"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-6 (induced by source paper "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"):
  with members:
    - "joint weakly and semi supervised deep learning for localization and classification of masses in breast ultrasound images"
    - "attention based deep multiple instance learning"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"
    - "unsupervised anomaly detection with generative adversarial networks to guide marker discovery"
    - "multisource transfer learning with convolutional neural networks for lung pattern analysis"
    - "deep learning with permutation invariant operator for multi instance histopathology classification"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "risk stratification of lung nodules using 3d cnn based multi task learning"

Connected through hyperedge HE-7 (induced by source paper "active learning using deep bayesian networks for surgical workflow analysis"):
  with members:
    - "temporal coherence based self supervised learning for laparoscopic workflow analysis"
    - "less is more surgical phase recognition with less annotations through self supervised pre training of cnn lstm networks"
    - "deepphase surgical phase recognition in cataracts videos"
    - "on calibration of modern neural networks"
    - "unsupervised temporal context learning using convolutional neural networks for laparoscopic workflow analysis"
    - "deep bayesian active learning with image data"
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

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)