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: "detection aided liver lesion segmentation using deep learning"
  Abstract: A fully automatic technique for segmenting the liver and localizing its unhealthy tissues is a convenient tool in order to diagnose hepatic diseases and assess the response to the according treatments. In this work we propose a method to segment the liver and its lesions from Computed Tomography (CT) scans using Convolutional Neural Networks (CNNs), that have proven good results in a variety of computer vision tasks, including medical imaging. The network that segments the lesions consists of a cascaded architecture, which first focuses on the region of the liver in order to segment the lesions on it. Moreover, we train a detector to localize the lesions, and mask the results of the segmentation network with the positive detections. The segmentation architecture is based on DRIU, a Fully Convolutional Network (FCN) with side outputs that work on feature maps of different resolutions, to finally benefit from the multi-scale information learned by different stages of the network. The main contribution of this work is the use of a detector to localize the lesions, which we show to be beneficial to remove false positives triggered by the segmentation network. Source code and models are available at this https URL .

Connected through hyperedge HE-1 (induced by source paper "deep q learning driven ct pancreas segmentation with geometry aware u net"):
  with members:
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "a 3d coarse to fine framework for volumetric medical image segmentation"
    - "deformable convolutional networks"
    - "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach"
    - "spatial aggregation of holistically nested convolutional neural networks for automated pancreas localization and segmentation"
    - "tree structured reinforcement learning for sequential object localization"
    - "spatial aggregation of holistically nested networks for automated pancreas segmentation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"

Connected through hyperedge HE-2 (induced by source paper "feature fusion encoder decoder network for automatic liver lesion segmentation"):
  with members:
    - "exfuse enhancing feature fusion for semantic segmentation"
    - "mr based synthetic ct generation using a deep convolutional neural network method"
    - "squeeze and excitation networks"
    - "automatic liver lesion detection using cascaded deep residual networks"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "3d deeply supervised network for automatic liver segmentation from ct volumes"
    - "automatic liver and lesion segmentation in ct using cascaded fully convolutional neural networks and 3d conditional random fields"

Connected through hyperedge HE-3 (induced by source paper "ra unet a hybrid deep attention aware network to extract liver and tumor in ct scans"):
  with members:
    - "segmentation of liver lesions with reduced complexity deep models"
    - "recurrent residual convolutional neural network based on u net r2u net for medical image segmentation"
    - "2d densely connected convolution neural networks for automatic liver and tumor segmentation"
    - "attention u net learning where to look for the pancreas"
    - "attention gated networks for improving ultrasound scan plane detection"
    - "attention is all you need"
    - "survivalnet predicting patient survival from diffusion weighted magnetic resonance images using cascaded fully convolutional and 3d convolutional neural networks"
    - "hierarchical convolutional deconvolutional neural networks for automatic liver and tumor segmentation"

Connected through hyperedge HE-4 (induced by source paper "segmentation of liver lesions with reduced complexity deep models"):
  with members:
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "mobilenets efficient convolutional neural networks for mobile vision applications"
    - "h denseunet hybrid densely connected unet for liver and liver tumor segmentation from ct volumes"
    - "mr based synthetic ct generation using a deep convolutional neural network method"
    - "squeeze and excitation networks"
    - "liver lesion segmentation informed by joint liver segmentation"
    - "aggregated residual transformations for deep neural networks"
    - "inception v4 inception resnet and the impact of residual connections on learning"

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)