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: "3d deeply supervised network for automatic liver segmentation from ct volumes"
  Abstract: Automatic liver segmentation from CT volumes is a crucial prerequisite yet challenging task for computer-aided hepatic disease diagnosis and treatment. In this paper, we present a novel 3D deeply supervised network (3D DSN) to address this challenging task. The proposed 3D DSN takes advantage of a fully convolutional architecture which performs efficient end-to-end learning and inference. More importantly, we introduce a deep supervision mechanism during the learning process to combat potential optimization difficulties, and thus the model can acquire a much faster convergence rate and more powerful discrimination capability. On top of the high-quality score map produced by the 3D DSN, a conditional random field model is further employed to obtain refined segmentation results. We evaluated our framework on the public MICCAI-SLiver07 dataset. Extensive experiments demonstrated that our method achieves competitive segmentation results to state-of-the-art approaches with a much faster processing speed.

Connected through hyperedge HE-1 (induced by source paper "abdominal multi organ segmentation with cascaded convolutional and adversarial deep networks"):
  with members:
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network"
    - "a multi scale pyramid of 3d fully convolutional networks for abdominal multi organ segmentation"
    - "unet a nested u net architecture for medical image segmentation"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "auto context convolutional neural network auto net for brain extraction in magnetic resonance imaging"
    - "hierarchical 3d fully convolutional networks for multi organ segmentation"

Connected through hyperedge HE-2 (induced by source paper "dlbc a deep learning based consensus in blockchains for deep learning services"):
  with members:
    - "energy recycling blockchain with proof of deep learning"
    - "exploiting computation power of blockchain for biomedical image segmentation"
    - "coin ai a proof of useful work scheme for blockchain based distributed deep learning"
    - "proof of search combining blockchain consensus formation with solving optimization problems"
    - "scnn a general distribution based statistical convolutional neural network with application to video object detection"
    - "privacy aware cost effective scheduling considering non schedulable appliances in smart home"
    - "real time adversarial attacks"
    - "measuring human perception to improve handwritten document transcription"

Connected through hyperedge HE-3 (induced by source paper "prior aware neural network for partially supervised multi organ segmentation"):
  with members:
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "constrained cnn losses for weakly supervised segmentation"
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "semi supervised multi organ segmentation via deep multi planar co training"
    - "a multi scale pyramid of 3d fully convolutional networks for abdominal multi organ segmentation"
    - "normalized cut loss for weakly supervised cnn segmentation"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "anatomical priors in convolutional networks for unsupervised biomedical segmentation"

Connected through hyperedge HE-4 (induced by source paper "be your own teacher improve the performance of convolutional neural networks via self distillation"):
  with members:
    - "slimmable neural networks"
    - "ktan knowledge transfer adversarial network"
    - "stochastic downsampling for cost adjustable inference and improved regularization in convolutional networks"
    - "meal multi model ensemble via adversarial learning"
    - "label refinery improving imagenet classification through label progression"
    - "skipnet learning dynamic routing in convolutional networks"
    - "focal loss for dense object detection"
    - "deep mutual learning"

Connected through hyperedge HE-5 (induced by source paper "ce net context encoder network for 2d medical image segmentation"):
  with members:
    - "rethinking imagenet pre training"
    - "fully convolutional adaptation networks for semantic segmentation"
    - "infinet fully convolutional networks for infant brain mri segmentation"
    - "joint optic disc and cup segmentation based on multi label deep network and polar transformation"
    - "sparse range constrained learning and its application for medical image grading"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "optic disc and cup segmentation methods for glaucoma detection with modification of u net convolutional neural network"
    - "pathological oct retinal layer segmentation using branch residual u shape networks"

Connected through hyperedge HE-6 (induced by source paper "probability map guided bi directional recurrent unet for pancreas segmentation"):
  with members:
    - "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification"
    - "pancreas segmentation in ct and mri images via domain specific network designing and recurrent neural contextual learning"
    - "ra unet a hybrid deep attention aware network to extract liver and tumor in ct scans"
    - "an application of cascaded 3d fully convolutional networks for medical image segmentation"
    - "3d semi supervised learning with uncertainty aware multi view co training"
    - "attention u net learning where to look for the pancreas"
    - "automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"

Connected through hyperedge HE-7 (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"
    - "detection aided liver lesion segmentation using deep learning"
    - "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"

Connected through hyperedge HE-8 (induced by source paper "deep sequential segmentation of organs in volumetric medical scans"):
  with members:
    - "densely dilated spatial pooling convolutional network using benign loss functions for imbalanced volumetric prostate segmentation"
    - "lstm a search space odyssey"
    - "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets"
    - "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
    - "an exploration of 2d and 3d deep learning techniques for cardiac mr image segmentation"
    - "h denseunet hybrid densely connected unet for liver and liver tumor segmentation from ct volumes"
    - "extracting 3d vascular structures from microscopy images using convolutional recurrent networks"
    - "automatic brain tumor detection and segmentation using u net based fully convolutional networks"

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)