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: "interactive medical image segmentation using deep learning with image specific fine tuning"
  Abstract: Convolutional neural networks (CNNs) have achieved state-of-the-art performance for automatic medical image segmentation. However, they have not demonstrated sufficiently accurate and robust results for clinical use. In addition, they are limited by the lack of image-specific adaptation and the lack of generalizability to previously unseen object classes (a.k.a. zero-shot learning). To address these problems, we propose a novel deep learning-based interactive segmentation framework by incorporating CNNs into a bounding box and scribble-based segmentation pipeline. We propose image-specific fine tuning to make a CNN model adaptive to a specific test image, which can be either unsupervised (without additional user interactions) or supervised (with additional scribbles). We also propose a weighted loss function considering network and interaction-based uncertainty for the fine tuning. We applied this framework to two applications: 2-D segmentation of multiple organs from fetal magnetic resonance (MR) slices, where only two types of these organs were annotated for training and 3-D segmentation of brain tumor core (excluding edema) and whole brain tumor (including edema) from different MR sequences, where only the tumor core in one MR sequence was annotated for training. Experimental results show that: 1) our model is more robust to segment previously unseen objects than state-of-the-art CNNs; 2) image-specific fine tuning with the proposed weighted loss function significantly improves segmentation accuracy; and 3) our method leads to accurate results with fewer user interactions and less user time than traditional interactive segmentation methods.

Connected through hyperedge HE-1 (induced by source paper "bi directional dermoscopic feature learning and multi scale consistent decision fusion for skin lesion segmentation"):
  with members:
    - "supervised saliency map driven segmentation of lesions in dermoscopic images"
    - "feature boosting network for 3d pose estimation"
    - "ce net context encoder network for 2d medical image segmentation"
    - "semantic correlation promoted shape variant context for segmentation"
    - "et net a generic edge attention guidance network for medical image segmentation"
    - "improving dermoscopic image segmentation with enhanced convolutional deconvolutional networks"
    - "boundary aware feature propagation for scene segmentation"
    - "attention gated networks learning to leverage salient regions in medical images"

Connected through hyperedge HE-2 (induced by source paper "a survey on active learning and human in the loop deep learning for medical image analysis"):
  with members:
    - "aleatoric uncertainty estimation with test time augmentation for medical image segmentation with convolutional neural networks"
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "continual lifelong learning with neural networks a review"
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "generative adversarial network in medical imaging a review"
    - "geometry in active learning for binary and multi class image segmentation"
    - "an overview of deep learning in medical imaging focusing on mri"
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"

Connected through hyperedge HE-3 (induced by source paper "machine learning at the network edge a survey"):
  with members:
    - "energy efficient radio resource allocation for federated edge learning"
    - "network offloading policies for cloud robotics a learning based approach"
    - "deep learning at the edge"
    - "edge intelligence paving the last mile of artificial intelligence with edge computing"
    - "edgelens deep learning based object detection in integrated iot fog and cloud computing environments"
    - "dabnn a super fast inference framework for binary neural networks on arm devices"
    - "pcamp performance comparison of machine learning packages on the edges"
    - "fastgrnn a fast accurate stable and tiny kilobyte sized gated recurrent neural network"

Connected through hyperedge HE-4 (induced by source paper "aleatoric uncertainty estimation with test time augmentation for medical image segmentation with convolutional neural networks"):
  with members:
    - "inherent brain segmentation quality control from fully convnet monte carlo sampling"
    - "exploring uncertainty measures in deep networks for multiple sclerosis lesion detection and segmentation"
    - "niftynet a deep learning platform for medical imaging"
    - "real time automatic fetal brain extraction in fetal mri by deep learning"
    - "automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks"
    - "auto context convolutional neural network auto net for brain extraction in magnetic resonance imaging"
    - "data distillation towards omni supervised learning"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"

Connected through hyperedge HE-5 (induced by source paper "weakly supervised segmentation from extreme points"):
  with members:
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "interactive segmentation of medical images through fully convolutional neural networks"
    - "constrained cnn losses for weakly supervised segmentation"
    - "accurate weakly supervised deep lesion segmentation on ct scans self paced 3d mask generation from recist"
    - "self learning to detect and segment cysts in lung ct images without manual annotation"
    - "accurate weakly supervised deep lesion segmentation using large scale clinical annotations slice propagated 3d mask generation from 2d recist"
    - "learning to segment medical images with scribble supervision alone"
    - "deep extreme cut from extreme points to object segmentation"

Connected through hyperedge HE-6 (induced by source paper "iteratively refined interactive 3d medical image segmentation with multi agent reinforcement learning"):
  with members:
    - "deepigeos a deep interactive geodesic framework for medical image segmentation"
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "fully convolutional network with multi step reinforcement learning for image processing"
    - "unet a nested u net architecture for medical image segmentation"
    - "iterative interaction training for segmentation editing networks"
    - "on the compactness efficiency and representation of 3d convolutional networks brain parcellation as a pretext task"
    - "annotating object instances with a polygon rnn"
    - "deepcut object segmentation from bounding box annotations using convolutional neural networks"

Connected through hyperedge HE-7 (induced by source paper "radiotherapy target contouring with convolutional gated graph neural network"):
  with members:
    - "efficient interactive annotation of segmentation datasets with polygon rnn"
    - "spatial temporal graph convolutional networks for skeleton based action recognition"
    - "brain tumor segmentation with deep neural networks"
    - "joint sequence learning and cross modality convolution for 3d biomedical segmentation"
    - "ui net interactive artificial neural networks for iterative image segmentation based on a user model"
    - "combining fully convolutional and recurrent neural networks for 3d biomedical image segmentation"
    - "gated graph sequence neural networks"
    - "3d u net learning dense volumetric segmentation from sparse annotation"

Connected through hyperedge HE-8 (induced by source paper "fetusmap fetal pose estimation in 3d ultrasound"):
  with members:
    - "feature boosting network for 3d pose estimation"
    - "less is more simultaneous view classification and landmark detection for abdominal ultrasound images"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "sononet real time detection and localisation of fetal standard scan planes in freehand ultrasound"
    - "training deep nets with sublinear memory cost"
    - "u net convolutional networks for biomedical image segmentation"

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)