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: "less is more simultaneous view classification and landmark detection for abdominal ultrasound images"
  Abstract: An abdominal ultrasound examination, which is the most common ultrasound examination, requires substantial manual efforts to acquire standard abdominal organ views, annotate the views in texts, and record clinically relevant organ measurements. Hence, automatic view classification and landmark detection of the organs can be instrumental to streamline the examination workflow. However, this is a challenging problem given not only the inherent difficulties from the ultrasound modality, e.g., low contrast and large variations, but also the heterogeneity across tasks, i.e., one classification task for all views, and then one landmark detection task for each relevant view. While convolutional neural networks (CNN) have demonstrated more promising outcomes on ultrasound image analytics than traditional machine learning approaches, it becomes impractical to deploy multiple networks (one for each task) due to the limited computational and memory resources on most existing ultrasound scanners. To overcome such limits, we propose a multi-task learning framework to handle all the tasks by a single network. This network is integrated to perform view classification and landmark detection simultaneously; it is also equipped with global convolutional kernels, coordinate constraints, and a conditional adversarial module to leverage the performances. In an experimental study based on 187,219 ultrasound images, with the proposed simplified approach we achieve (1) view classification accuracy better than the agreement between two clinical experts and (2) landmark-based measurement errors on par with inter-user variability. The multi-task approach also benefits from sharing the feature extraction during the training process across all tasks and, as a result, outperforms the approaches that address each task individually.

Connected through hyperedge HE-1 (induced by source paper "oralcam enabling self examination and awareness of oral health using a smartphone camera"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "visual interpretability for deep learning a survey"
    - "tienet text image embedding network for common thorax disease classification and reporting in chest x rays"
    - "mdnet a semantically and visually interpretable medical image diagnosis network"
    - "a survey on deep learning in medical image analysis"
    - "ssd single shot multibox detector"
    - "yolo9000 better faster stronger"
    - "an all in one convolutional neural network for face analysis"

Connected through hyperedge HE-2 (induced by source paper "fetusmap fetal pose estimation in 3d ultrasound"):
  with members:
    - "feature boosting network for 3d pose estimation"
    - "interactive medical image segmentation using deep learning with image specific fine tuning"
    - "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"

Connected through hyperedge HE-3 (induced by source paper "splenomegaly segmentation on multi modal mri using deep convolutional networks"):
  with members:
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "deep supervision for pancreatic cyst segmentation in abdominal ct scans"
    - "improving deep pancreas segmentation in ct and mri images via recurrent neural contextual learning and direct loss function"
    - "splenomegaly segmentation using global convolutional kernels and conditional generative adversarial networks"
    - "stargan unified generative adversarial networks for multi domain image to image translation"
    - "spatial aggregation of holistically nested convolutional neural networks for automated pancreas localization and segmentation"
    - "photo realistic single image super resolution using a generative adversarial network"
    - "semantic segmentation using adversarial networks"

Connected through hyperedge HE-4 (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"
    - "attention gated networks for improving ultrasound scan plane detection"
    - "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)