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: "iterative multi domain regularized deep learning for anatomical structure detection and segmentation from ultrasound images"
  Abstract: Accurate detection and segmentation of anatomical structures from ultrasound images are crucial for clinical diagnosis and biometric measurements. Although ultrasound imaging has been widely used with superiorities such as low cost and portability, the fuzzy border definition and existence of abounding artifacts pose great challenges for automatically detecting and segmenting the complex anatomical structures. In this paper, we propose a multi-domain regularized deep learning method to address this challenging problem. By leveraging the transfer learning from cross domains, the feature representations are effectively enhanced. The results are further improved by the iterative refinement. Moreover, our method is quite efficient by taking advantage of a fully convolutional network, which is formulated as an end-to-end learning framework of detection and segmentation. Extensive experimental results on a large-scale database corroborated that our method achieved a superior detection and segmentation accuracy, outperforming other methods by a significant margin and demonstrating competitive capability even compared to human performance.

Connected through hyperedge HE-1 (induced by source paper "deep learning in cardiology"):
  with members:
    - "pixelbnn augmenting the pixelcnn with batch normalization and the presentation of a fast architecture for retinal vessel segmentation"
    - "estimation of the volume of the left ventricle from mri images using deep neural networks"
    - "deep learning analysis of the myocardium in coronary ct angiography for identification of patients with functionally significant coronary artery stenosis"
    - "convolutional capsule network for classification of breast cancer histology images"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"
    - "brain tumor type classification via capsule networks"
    - "deepheart semi supervised sequence learning for cardiovascular risk prediction"

Connected through hyperedge HE-2 (induced by source paper "maskplus improving mask generation for instance segmentation"):
  with members:
    - "hybrid task cascade for instance segmentation"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "path aggregation network for instance segmentation"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "recurrent pixel embedding for instance grouping"
    - "cascade r cnn delving into high quality object detection"
    - "gland instance segmentation using deep multichannel neural networks"
    - "masklab instance segmentation by refining object detection with semantic and direction features"

Connected through hyperedge HE-3 (induced by source paper "sparsely activated networks a new method for decomposing and compressing data"):
  with members:
    - "deep learning analysis of the myocardium in coronary ct angiography for identification of patients with functionally significant coronary artery stenosis"
    - "convolutional capsule network for classification of breast cancer histology images"
    - "predicting cardiovascular risk factors in retinal fundus photographs using deep learning"
    - "brain tumor type classification via capsule networks"
    - "automatic calcium scoring in low dose chest ct using deep neural networks with dilated convolutions"
    - "medical image analysis using convolutional neural networks a review"
    - "beat by beat classifying cardiac arrhythmias with recurrent neural networks"
    - "recognizing abnormal heart sounds using deep learning"

Connected through hyperedge HE-4 (induced by source paper "tan temporal affine network for real time left ventricle anatomical structure analysis based on 2d ultrasound videos"):
  with members:
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "voxelatlasgan 3d left ventricle segmentation on echocardiography with atlas guided generation and voxel to voxel discrimination"
    - "a comprehensive approach for learning based fully automated inter slice motion correction for short axis cine cardiac mr image stacks"
    - "left ventricle segmentation via optical flow net from short axis cine mri preserving the temporal coherence of cardiac motion"
    - "left ventricle segmentation and quantification from cardiac cine mr images via multi task learning"
    - "iterative segmentation from limited training data applications to congenital heart disease"
    - "supervision by registration an unsupervised approach to improve the precision of facial landmark detectors"
    - "automated segmentation on the entire cardiac cycle using a deep learning work flow"

Connected through hyperedge HE-5 (induced by source paper "joint weakly and semi supervised deep learning for localization and classification of masses in breast ultrasound images"):
  with members:
    - "hand pose estimation through semi supervised and weakly supervised learning"
    - "deep residual learning for image recognition"
    - "fast r cnn"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "learning deep features for discriminative localization"
    - "adam a method for stochastic optimization"
    - "very deep convolutional networks for large scale image recognition"
    - "weakly supervised discovery of visual pattern configurations"

Connected through hyperedge HE-6 (induced by source paper "anatomically constrained neural networks acnns application to cardiac image enhancement and segmentation"):
  with members:
    - "a survey on deep learning in medical image analysis"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "perceptual losses for real time style transfer and super resolution"
    - "vconv dae deep volumetric shape learning without object labels"
    - "accelerating the super resolution convolutional neural network"
    - "learning a predictable and generative vector representation for objects"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "semantic segmentation using adversarial networks"

Connected through hyperedge HE-7 (induced by source paper "fully automatic segmentation of 3d brain ultrasound learning from coarse annotations"):
  with members:
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "hough cnn deep learning for segmentation of deep brain regions in mri and ultrasound"

Connected through hyperedge HE-8 (induced by source paper "fine grained recurrent neural networks for automatic prostate segmentation in ultrasound images"):
  with members:
    - "amodal instance segmentation"
    - "theano a python framework for fast computation of mathematical expressions"
    - "very deep convolutional networks for large scale image recognition"
    - "fully convolutional networks for semantic 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)