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: "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
  Abstract: Automatic diagnosing lung cancer from computed tomography scans involves two steps: detect all suspicious lesions (pulmonary nodules) and evaluate the whole-lung/pulmonary malignancy. Currently, there are many studies about the first step, but few about the second step. Since the existence of nodule does not definitely indicate cancer, and the morphology of nodule has a complicated relationship with cancer, the diagnosis of lung cancer demands careful investigations on every suspicious nodule and integration of information of all nodules. We propose a 3-D deep neural network to solve this problem. The model consists of two modules. The first one is a 3-D region proposal network for nodule detection, which outputs all suspicious nodules for a subject. The second one selects the top five nodules based on the detection confidence, evaluates their cancer probabilities, and combines them with a leaky noisy-OR gate to obtain the probability of lung cancer for the subject. The two modules share the same backbone network, a modified U-net. The overfitting caused by the shortage of the training data is alleviated by training the two modules alternately. The proposed model won the first place in the Data Science Bowl 2017 competition.

Connected through hyperedge HE-1 (induced by source paper "3d aggregated faster r cnn for general lesion detection"):
  with members:
    - "mini lesions detection on diabetic retinopathy images via large scale cnn features"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "applications of deep learning and reinforcement learning to biological data"
    - "towards single phase single stage detection of pulmonary nodules in chest ct imaging"
    - "focal loss for dense object detection"
    - "dssd deconvolutional single shot detector"
    - "deep lesion graphs in the wild relationship learning and organization of significant radiology image findings in a diverse large scale lesion database"
    - "accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks"

Connected through hyperedge HE-2 (induced by source paper "deep learning for automated medical image analysis"):
  with members:
    - "anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy"
    - "deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy"
    - "deepem deep 3d convnets with em for weakly supervised pulmonary nodule detection"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "3d segmentation with exponential logarithmic loss for highly unbalanced object sizes"
    - "focal loss for dense object detection"
    - "the microsoft 2017 conversational speech recognition system"

Connected through hyperedge HE-3 (induced by source paper "a 3d probabilistic deep learning system for detection and diagnosis of lung cancer using low dose ct scans"):
  with members:
    - "attention based deep multiple instance learning"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "towards radiologist level cancer risk assessment in ct lung screening using deep learning"
    - "s4nd single shot single scale lung nodule detection"
    - "risk stratification of lung nodules using 3d cnn based multi task learning"
    - "automated pulmonary nodule detection via 3d convnets with online sample filtering and hybrid loss residual learning"
    - "accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks"
    - "cased curriculum adaptive sampling for extreme data imbalance"

Connected through hyperedge HE-4 (induced by source paper "nodulenet decoupled false positive reductionfor pulmonary nodule detection and segmentation"):
  with members:
    - "an end to end framework for integrated pulmonary nodule detection and false positive reduction"
    - "automated pulmonary nodule detection using 3d deep convolutional neural networks"
    - "revisiting rcnn on awakening the classification power of faster rcnn"
    - "joint learning for pulmonary nodule segmentation attributes and malignancy prediction"
    - "s4nd single shot single scale lung nodule detection"
    - "iw net an automatic and minimalistic interactive lung nodule segmentation deep network"
    - "deeplung 3d deep convolutional nets for automated pulmonary nodule detection and classification"
    - "accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "attentive ct lesion detection using deep pyramid inference with multi scale booster"):
  with members:
    - "joint face hallucination and deblurring via structure generation and detail enhancement"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"
    - "3d context enhanced region based convolutional neural network for end to end lesion detection"
    - "squeeze and excitation networks"
    - "deeplesion automated deep mining categorization and detection of significant radiology image findings using large scale clinical lesion annotations"
    - "accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks"
    - "fast preprocessing for robust face sketch synthesis"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"

Connected through hyperedge HE-6 (induced by source paper "3d anchor free lesion detector on computed tomography scans"):
  with members:
    - "objects as points"
    - "cornernet detecting objects as paired keypoints"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "towards single phase single stage detection of pulmonary nodules in chest ct imaging"
    - "focal loss for dense object detection"
    - "deep lesion graphs in the wild relationship learning and organization of significant radiology image findings in a diverse large scale lesion database"
    - "validation comparison and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images the luna16 challenge"
    - "feature pyramid networks for object detection"

Connected through hyperedge HE-7 (induced by source paper "mulan multitask universal lesion analysis network for joint lesion detection tagging and segmentation"):
  with members:
    - "holistic and comprehensive annotation of clinically significant findings on diverse ct images learning from radiology reports and label ontology"
    - "uldor a universal lesion detector for ct scans with pseudo masks and hard negative example mining"
    - "joint learning for pulmonary nodule segmentation attributes and malignancy prediction"
    - "3d context enhanced region based convolutional neural network for end to end lesion detection"
    - "semi automatic recist labeling on ct scans with cascaded convolutional neural networks"
    - "mask r cnn"
    - "detecting and classifying lesions in mammograms with deep learning"
    - "densely connected convolutional networks"

Connected through hyperedge HE-8 (induced by source paper "deepseed 3d squeeze and excitation encoder decoder convnets for pulmonary nodule detection"):
  with members:
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "s4nd single shot single scale lung nodule detection"
    - "ct realistic lung nodule simulation from 3d conditional generative adversarial networks for robust lung segmentation"
    - "focal loss for dense object detection"
    - "automated pulmonary nodule detection via 3d convnets with online sample filtering and hybrid loss residual learning"
    - "squeeze and excitation networks"
    - "accurate pulmonary nodule detection in computed tomography images using deep convolutional neural networks"
    - "validation comparison and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images the luna16 challenge"

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)