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: "gp unet lesion detection from weak labels with a 3d regression network"
  Abstract: We propose a novel convolutional neural network for lesion detection from weak labels. Only a single, global label per image - the lesion count - is needed for training. We train a regression network with a fully convolutional architecture combined with a global pooling layer to aggregate the 3D output into a scalar indicating the lesion count. When testing on unseen images, we first run the network to estimate the number of lesions. Then we remove the global pooling layer to compute localization maps of the size of the input image. We evaluate the proposed network on the detection of enlarged perivascular spaces in the basal ganglia in MRI. Our method achieves a sensitivity of 62% with on average 1.5 false positives per image. Compared with four other approaches based on intensity thresholding, saliency and class maps, our method has a 20% higher sensitivity.

Connected through hyperedge HE-1 (induced by source paper "weakly supervised object detection with 2d and 3d regression neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "self paced deep learning for weakly supervised object detection"
    - "visual interpretability for deep learning a survey"
    - "attention based deep multiple instance learning"
    - "model agnostic saliency for weakly supervised lesion detection from breast dce mri"
    - "deep learning from label proportions for emphysema quantification"
    - "weakly supervised salient object detection using image labels"
    - "attention u net learning where to look for the pancreas"

Connected through hyperedge HE-2 (induced by source paper "da refinenet a dual input whole slide image segmentation algorithm based on attention"):
  with members:
    - "learning a discriminative feature network for semantic segmentation"
    - "multi resolution networks for semantic segmentation in whole slide images"
    - "reinforced auto zoom net towards accurate and fast breast cancer segmentation in whole slide images"
    - "assessment of breast cancer histology using densely connected convolutional networks"
    - "improving whole slide segmentation through visual context a systematic study"
    - "attention is all you need"
    - "deep learning applied to nlp"
    - "squeeze and excitation networks"

Connected through hyperedge HE-3 (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-4 (induced by source paper "pre and post hoc diagnosis and interpretation of malignancy from breast dce mri"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "model agnostic saliency for weakly supervised lesion detection from breast dce mri"
    - "training medical image analysis systems like radiologists"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "teacher student curriculum learning"
    - "thoracic disease identification and localization with limited supervision"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "discriminative localization in cnns for weakly supervised segmentation of pulmonary nodules"

Connected through hyperedge HE-5 (induced by source paper "3d regression neural network for the quantification of enlarged perivascular spaces in brain mri"):
  with members:
    - "segmentation of intracranial arterial calcification with deeply supervised residual dropout networks"
    - "voxresnet deep voxelwise residual networks for volumetric brain segmentation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "end to end instance segmentation with recurrent attention"
    - "real time 2d 3d registration via cnn regression"
    - "learning to count with deep object features"
    - "very deep convolutional networks for large scale image recognition"
    - "recurrent models of visual attention"

Connected through hyperedge HE-6 (induced by source paper "subject2vec generative discriminative approach from a set of image patches to a vector"):
  with members:
    - "deep sets"
    - "transfer learning for multi center classification of chronic obstructive pulmonary disease"
    - "effective approaches to attention based neural machine translation"
    - "show attend and tell neural image caption generation with visual attention"
    - "fast and accurate deep network learning by exponential linear units elus"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-7 (induced by source paper "quantification of lung abnormalities in cystic fibrosis using deep networks"):
  with members:
    - "u net convolutional networks for biomedical image segmentation"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"
    - "network in network"

Connected through hyperedge HE-8 (induced by source paper "deep learning from label proportions for emphysema quantification"):
  with members:
    - "deep residual learning for image recognition"

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)