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: "sononet real time detection and localisation of fetal standard scan planes in freehand ultrasound"
  Abstract: Identifying and interpreting fetal standard scan planes during 2D ultrasound mid-pregnancy examinations are highly complex tasks which require years of training. Apart from guiding the probe to the correct location, it can be equally difficult for a non-expert to identify relevant structures within the image. Automatic image processing can provide tools to help experienced as well as inexperienced operators with these tasks. In this paper, we propose a novel method based on convolutional neural networks which can automatically detect 13 fetal standard views in freehand 2D ultrasound data as well as provide a localisation of the fetal structures via a bounding box. An important contribution is that the network learns to localise the target anatomy using weak supervision based on image-level labels only. The network architecture is designed to operate in real-time while providing optimal output for the localisation task. We present results for real-time annotation, retrospective frame retrieval from saved videos, and localisation on a very large and challenging dataset consisting of images and video recordings of full clinical anomaly screenings. We found that the proposed method achieved an average F1-score of 0.798 in a realistic classification experiment modelling real-time detection, and obtained a 90.09% accuracy for retrospective frame retrieval. Moreover, an accuracy of 77.8% was achieved on the localisation task.

Connected through hyperedge HE-1 (induced by source paper "learning cross domain generalizable features by representation disentanglement"):
  with members:
    - "a two step disentanglement method"
    - "domain agnostic learning with disentangled representations"
    - "synergistic image and feature adaptation towards cross modality domain adaptation for medical image segmentation"
    - "sliced wasserstein discrepancy for unsupervised domain adaptation"
    - "semi supervised domain adaptation via minimax entropy"
    - "domain generalization via model agnostic learning of semantic features"
    - "mixmatch a holistic approach to semi supervised learning"
    - "representation disentanglement for multi task learning with application to fetal ultrasound"

Connected through hyperedge HE-2 (induced by source paper "self supervised representation learning for ultrasound video"):
  with members:
    - "ultrasound image representation learning by modeling sonographer visual attention"
    - "squeeze and excitation networks"
    - "colorful image colorization"
    - "what do different evaluation metrics tell us about saliency models"
    - "unsupervised learning of visual representations using videos"

Connected through hyperedge HE-3 (induced by source paper "model based and data driven strategies in medical image computing"):
  with members:
    - "deep learning cardiac motion analysis for human survival prediction"
    - "transparency motivations and challenges"
    - "learning a probabilistic model for diffeomorphic registration"
    - "explicit topological priors for deep learning based image segmentation using persistent homology"
    - "towards federated learning at scale system design"
    - "quantifying and leveraging classification uncertainty for chest radiograph assessment"
    - "probabilistic motion modeling from medical image sequences application to cardiac cine mri"
    - "a deep learning framework for unsupervised affine and deformable image registration"

Connected through hyperedge HE-4 (induced by source paper "ultrasound image representation learning by modeling sonographer visual attention"):
  with members:
    - "do better imagenet models transfer better"
    - "saliency prediction in the deep learning era an empirical investigation"
    - "attention gated networks for improving ultrasound scan plane detection"
    - "squeeze and excitation networks"
    - "aggregated residual transformations for deep neural networks"
    - "shuffle and learn unsupervised learning using temporal order verification"
    - "unsupervised visual representation learning by context prediction"
    - "deepfix a fully convolutional neural network for predicting human eye fixations"

Connected through hyperedge HE-5 (induced by source paper "automatic quality assessment for 2d fetal sonographic standard plane based on multi task learning"):
  with members:
    - "focal loss for dense object detection"
    - "feature pyramid networks for object detection"
    - "you only look once unified real time object detection"
    - "deep residual learning for image recognition"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "very deep convolutional networks for large scale image recognition"
    - "spatial pyramid pooling in deep convolutional networks for visual recognition"

Connected through hyperedge HE-6 (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"
    - "interactive medical image segmentation using deep learning with image specific fine tuning"
    - "adversarial posenet a structure aware convolutional network for human pose estimation"
    - "training deep nets with sublinear memory cost"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-7 (induced by source paper "semi supervised learning of fetal anatomy from ultrasound"):
  with members:
    - "unsupervised data augmentation for consistency training"
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "there are many consistent explanations of unlabeled data why you should average"
    - "autoaugment learning augmentation policies from data"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "temporal ensembling for semi supervised learning"

Connected through hyperedge HE-8 (induced by source paper "attention gated networks for improving ultrasound scan plane detection"):
  with members:
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "diagnose like a radiologist attention guided convolutional neural network for thorax disease classification"
    - "learn to pay attention"
    - "tandemnet distilling knowledge from medical images using diagnostic reports as optional semantic references"
    - "non local neural networks"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "a survey on deep learning in medical image analysis"
    - "residual attention network for image classification"

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)