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: "understanding the mechanisms of deep transfer learning for medical images"
  Abstract: The ability to automatically learn task specific feature representations has led to a huge success of deep learning methods. When large training data is scarce, such as in medical imaging problems, transfer learning has been very effective. In this paper, we systematically investigate the process of transferring a Convolutional Neural Network, trained on ImageNet images to perform image classification, to kidney detection problem in ultrasound images. We study how the detection performance depends on the extent of transfer. We show that a transferred and tuned CNN can outperform a state-of-the-art feature engineered pipeline and a hybridization of these two techniques achieves 20\% higher performance. We also investigate how the evolution of intermediate response images from our network. Finally, we compare these responses to state-of-the-art image processing filters in order to gain greater insight into how transfer learning is able to effectively manage widely varying imaging regimes.

Connected through hyperedge HE-1 (induced by source paper "a deep learning framework for single sided sound speed inversion in medical ultrasound"):
  with members:
    - "compressed sensing mri reconstruction using a generative adversarial network with a cyclic loss"
    - "low dose ct image denoising using a generative adversarial network with wasserstein distance and perceptual loss"
    - "a benchmark for breast ultrasound image segmentation busis"
    - "3 d convolutional encoder decoder network for low dose ct via transfer learning from a 2 d trained network"
    - "framing u net via deep convolutional framelets application to sparse view ct"
    - "brain tumor segmentation with deep neural networks"
    - "towards ct quality ultrasound imaging using deep learning"
    - "deep learning in rf sub sampled b mode ultrasound imaging"

Connected through hyperedge HE-2 (induced by source paper "med3d transfer learning for 3d medical image analysis"):
  with members:
    - "evaluation of algorithms for multi modality whole heart segmentation an open access grand challenge"
    - "rethinking imagenet pre training"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "mr based synthetic ct generation using a deep convolutional neural network method"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-3 (induced by source paper "generalization of feature embeddings transferred from different video anomaly detection domains"):
  with members:
    - "multi label learning based deep transfer neural network for facial attribute classification"
    - "unsupervised representation learning using convolutional and stacked auto encoders a domain and cross domain feature space analysis"
    - "learning cross modal deep representations for robust pedestrian detection"
    - "providing theoretical learning guarantees to deep learning networks"
    - "learning temporal regularity in video sequences"
    - "simultaneous deep transfer across domains and tasks"
    - "learning transferable features with deep adaptation networks"
    - "how transferable are features in deep neural networks"

Connected through hyperedge HE-4 (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-5 (induced by source paper "pneumonia detection in chest radiographs"):
  with members:
    - "couplenet coupling global structure with local parts for object detection"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "aggregated residual transformations for deep neural networks"
    - "densely connected convolutional networks"
    - "wide residual networks"
    - "spatial transformer networks"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-6 (induced by source paper "slidenet fast and accurate slide quality assessment based on deep neural networks"):
  with members:
    - "shufflenet an extremely efficient convolutional neural network for mobile devices"
    - "keynote small neural nets are beautiful enabling embedded systems with small deep neural network architectures"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "tv gan generative adversarial network based thermal to visible face recognition"
    - "squeezenet alexnet level accuracy with 50x fewer parameters and 0 5mb model size"
    - "deep residual learning for image recognition"
    - "segmental hog new descriptor for glomerulus detection in kidney microscopy image"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "plaque classification in coronary arteries from ivoct images using convolutional neural networks and transfer learning"):
  with members:
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-8 (induced by source paper "exploiting the potential of unlabeled endoscopic video data with self supervised learning"):
  with members:
    - "clickstream analysis for crowd based object segmentation with confidence"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "semi supervised deep learning for fully convolutional networks"
    - "unsupervised temporal context learning using convolutional neural networks for laparoscopic workflow analysis"
    - "colorization as a proxy task for visual understanding"
    - "surgical data science for next generation interventions"
    - "deep residual learning for instrument segmentation in robotic surgery"
    - "toolnet holistically nested real time segmentation of robotic surgical tools"

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)