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: "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
  Abstract: Machine learning (ML) algorithms have made a tremendous impact in the field of medical imaging. While medical imaging datasets have been growing in size, a challenge for supervised ML algorithms that is frequently mentioned is the lack of annotated data. As a result, various methods which can learn with less/other types of supervision, have been proposed. We review semi-supervised, multiple instance, and transfer learning in medical imaging, both in diagnosis/detection or segmentation tasks. We also discuss connections between these learning scenarios, and opportunities for future research.

Connected through hyperedge HE-1 (induced by source paper "a survey on semi self and unsupervised techniques in image classification"):
  with members:
    - "small data challenges in big data era a survey of recent progress on unsupervised and semi supervised methods"
    - "2d and 3d segmentation of uncertain local collagen fiber orientations in shg microscopy"
    - "improvability through semi supervised learning a survey of theoretical results"
    - "data efficient image recognition with contrastive predictive coding"
    - "self supervised visual feature learning with deep neural networks a survey"
    - "unsupervised data augmentation for consistency training"
    - "learning representations by maximizing mutual information across views"
    - "mixmatch a holistic approach to semi supervised learning"

Connected through hyperedge HE-2 (induced by source paper "lc gan image to image translation based on generative adversarial network for endoscopic images"):
  with members:
    - "2018 robotic scene segmentation challenge"
    - "towards better surgical instrument segmentation in endoscopic vision multi angle feature aggregation and contour supervision"
    - "attention guided lightweight network for real time segmentation of robotic surgical instruments"
    - "learning where to look while tracking instruments in robot assisted surgery"
    - "2017 robotic instrument segmentation challenge"
    - "multimodal unsupervised image to image translation"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "sem gan semantically consistent image to image translation"

Connected through hyperedge HE-3 (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"
    - "nnu net self adapting framework for u net based medical image segmentation"
    - "convolutional neural networks for medical image analysis full training or fine tuning"

Connected through hyperedge HE-4 (induced by source paper "train learn expand repeat"):
  with members:
    - "boxnet deep learning based biomedical image segmentation using boxes only annotation"
    - "development and validation of deep learning algorithms for detection of critical findings in head ct scans"
    - "hyperdense net a hyper densely connected cnn for multi modal image segmentation"
    - "scribblesup scribble supervised convolutional networks for semantic segmentation"
    - "simple does it weakly supervised instance and semantic segmentation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-5 (induced by source paper "a review of domain adaptation without target labels"):
  with members:
    - "regularized learning for domain adaptation under label shifts"
    - "sample to sample correspondence for unsupervised domain adaptation"
    - "unsupervised domain adaptation using regularized hyper graph matching"
    - "theoretical perspective of deep domain adaptation"
    - "deep visual domain adaptation a survey"
    - "characterizing and avoiding negative transfer"
    - "detecting and correcting for label shift with black box predictors"
    - "deepjdot deep joint distribution optimal transport for unsupervised domain adaptation"

Connected through hyperedge HE-6 (induced by source paper "a survey on active learning and human in the loop deep learning for medical image analysis"):
  with members:
    - "aleatoric uncertainty estimation with test time augmentation for medical image segmentation with convolutional neural networks"
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "continual lifelong learning with neural networks a review"
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "generative adversarial network in medical imaging a review"
    - "geometry in active learning for binary and multi class image segmentation"
    - "an overview of deep learning in medical imaging focusing on mri"
    - "annotation cost minimization for medical image segmentation using suggestive mixed supervision fully convolutional networks"

Connected through hyperedge HE-7 (induced by source paper "transformation consistent self ensembling model for semi supervised medical image segmentation"):
  with members:
    - "the liver tumor segmentation benchmark lits"
    - "unsupervised domain adaptation for automatic estimation of cardiothoracic ratio"
    - "adversarial learning for semi supervised semantic segmentation"
    - "unsupervised reverse domain adaptation for synthetic medical images via adversarial training"
    - "discriminative localization in cnns for weakly supervised segmentation of pulmonary nodules"
    - "isic 2017 skin lesion analysis towards melanoma detection"
    - "segmentation of optic disc fovea and retinal vasculature using a single convolutional neural network"
    - "weakly supervised localization of diabetic retinopathy lesions in retinal fundus images"

Connected through hyperedge HE-8 (induced by source paper "laptool net a contextual detector of surgical tools in laparoscopic videos based on recurrent convolutional neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "multi label classification of surgical tools with convolutional neural networks"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "a survey on deep learning in medical image analysis"
    - "tool and phase recognition using contextual cnn features"
    - "single and multi task architectures for tool presence detection challenge at m2cai 2016"
    - "inception v4 inception resnet and the impact of residual connections on learning"
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"

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)