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: "attention gated networks for improving ultrasound scan plane detection"
  Abstract: In this work, we apply an attention-gated network to real-time automated scan plane detection for fetal ultrasound screening. Scan plane detection in fetal ultrasound is a challenging problem due the poor image quality resulting in low interpretability for both clinicians and automated algorithms. To solve this, we propose incorporating self-gated soft-attention mechanisms. A soft-attention mechanism generates a gating signal that is end-to-end trainable, which allows the network to contextualise local information useful for prediction. The proposed attention mechanism is generic and it can be easily incorporated into any existing classification architectures, while only requiring a few additional parameters. We show that, when the base network has a high capacity, the incorporated attention mechanism can provide efficient object localisation while improving the overall performance. When the base network has a low capacity, the method greatly outperforms the baseline approach and significantly reduces false positives. Lastly, the generated attention maps allow us to understand the model's reasoning process, which can also be used for weakly supervised object localisation.

Connected through hyperedge HE-1 (induced by source paper "pdanet pyramid density aware attention net for accurate crowd counting"):
  with members:
    - "multi scale attention network for crowd counting"
    - "learn to scale generating multipolar normalized density map for crowd counting"
    - "learning from synthetic data for crowd counting in the wild"
    - "denet a universal network for counting crowd with varying densities and scales"
    - "divide and grow capturing huge diversity in crowd images with incrementally growing cnn"
    - "crowd counting using deep recurrent spatial aware network"
    - "context aware crowd counting"
    - "adaptive scenario discovery for crowd counting"

Connected through hyperedge HE-2 (induced by source paper "weakly supervised object detection with 2d and 3d regression neural networks"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "self paced deep learning for weakly supervised object detection"
    - "visual interpretability for deep learning a survey"
    - "attention based deep multiple instance learning"
    - "model agnostic saliency for weakly supervised lesion detection from breast dce mri"
    - "deep learning from label proportions for emphysema quantification"
    - "weakly supervised salient object detection using image labels"
    - "attention u net learning where to look for the pancreas"

Connected through hyperedge HE-3 (induced by source paper "cnns lstms and attention networks for pathology detection in medical data"):
  with members:
    - "attention u net learning where to look for the pancreas"
    - "learn to pay attention"
    - "chestx ray8 hospital scale chest x ray database and benchmarks on weakly supervised classification and localization of common thorax diseases"
    - "two phase learning for weakly supervised object localization"
    - "comparative study of cnn and rnn for natural language processing"
    - "interpretable explanations of black boxes by meaningful perturbation"
    - "convolutional recurrent neural networks for electrocardiogram classification"
    - "a gentle tutorial of recurrent neural network with error backpropagation"

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"
    - "squeeze and excitation networks"
    - "aggregated residual transformations for deep neural networks"
    - "shuffle and learn unsupervised learning using temporal order verification"
    - "sononet real time detection and localisation of fetal standard scan planes in freehand ultrasound"
    - "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 "lung cancer detection using co learning from chest ct images and clinical demographics"):
  with members:
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-6 (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"
    - "detection aided liver lesion segmentation using deep learning"
    - "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-7 (induced by source paper "coronary calcium detection using 3d attention identical dual deep network based on weakly supervised learning"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "synseg net synthetic segmentation without target modality ground truth"
    - "less is more simultaneous view classification and landmark detection for abdominal ultrasound images"
    - "inter scanner harmonization of high angular resolution dw mri using null space deep learning"
    - "an automatic deep learning approach for coronary artery calcium segmentation"
    - "splenomegaly segmentation using global convolutional kernels and conditional generative adversarial networks"
    - "adversarial synthesis learning enables segmentation without target modality ground truth"

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)