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: "deep structured learning for mass segmentation from mammograms"
  Abstract: In this paper, we present a novel method for the segmentation of breast masses from mammograms exploring structured and deep learning. Specifically, using structured support vector machine (SSVM), we formulate a model that combines different types of potential functions, including one that classifies image regions using deep learning. Our main goal with this work is to show the accuracy and efficiency improvements that these relatively new techniques can provide for the segmentation of breast masses from mammograms. We also propose an easily reproducible quantitative analysis to as- sess the performance of breast mass segmentation methodologies based on widely accepted accuracy and running time measurements on public datasets, which will facilitate further comparisons for this segmentation problem. In particular, we use two publicly available datasets (DDSM-BCRP and INbreast) and propose the computa- tion of the running time taken for the methodology to produce a mass segmentation given an input image and the use of the Dice index to quantitatively measure the segmentation accuracy. For both databases, we show that our proposed methodology produces competitive results in terms of accuracy and running time.

Connected through hyperedge HE-1 (induced by source paper "deep learning for automated medical image analysis"):
  with members:
    - "evaluate the malignancy of pulmonary nodules using the 3 d deep leaky noisy or network"
    - "anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy"
    - "deep learning to achieve clinically applicable segmentation of head and neck anatomy for radiotherapy"
    - "deepem deep 3d convnets with em for weakly supervised pulmonary nodule detection"
    - "deeplung deep 3d dual path nets for automated pulmonary nodule detection and classification"
    - "bert pre training of deep bidirectional transformers for language understanding"
    - "3d segmentation with exponential logarithmic loss for highly unbalanced object sizes"
    - "focal loss for dense object detection"

Connected through hyperedge HE-2 (induced by source paper "aunet attention guided dense upsampling networks for breast mass segmentation in whole mammograms"):
  with members:
    - "on the convergence of adam and beyond"
    - "y net joint segmentation and classification for diagnosis of breast biopsy images"
    - "star shape prior in fully convolutional networks for skin lesion segmentation"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"
    - "non local neural networks"
    - "attention is all you need"
    - "dilated residual networks"
    - "squeeze and excitation networks"

Connected through hyperedge HE-3 (induced by source paper "improved breast mass segmentation in mammograms with conditional residual u net"):
  with members:
    - "adversarial deep structured nets for mass segmentation from mammograms"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-4 (induced by source paper "a multi scale cnn and curriculum learning strategy for mammogram classification"):
  with members:
    - "high resolution breast cancer screening with multi view deep convolutional neural networks"
    - "optimizing and visualizing deep learning for benign malignant classification in breast tumors"
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "adversarial deep structural networks for mammographic mass segmentation"
    - "breast mass classification from mammograms using deep convolutional neural networks"
    - "wide residual networks"
    - "deep residual learning for image recognition"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

Connected through hyperedge HE-5 (induced by source paper "adversarial deep structured nets for mass segmentation from mammograms"):
  with members:
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "conditional random fields as recurrent neural networks"
    - "fully convolutional networks for semantic segmentation"
    - "intriguing properties of neural networks"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

Connected through hyperedge HE-6 (induced by source paper "adversarial deep structural networks for mammographic mass segmentation"):
  with members:
    - "deep multi instance networks with sparse label assignment for whole mammogram classification"
    - "co occurrence feature learning for skeleton based action recognition using regularized deep lstm networks"
    - "deepcut object segmentation from bounding box annotations using convolutional neural networks"
    - "deeply learning the messages in message passing inference"
    - "adam a method for stochastic optimization"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "fully convolutional networks for semantic segmentation"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-7 (induced by source paper "maximum margin structured learning with deep networks for 3d human pose estimation"):
  with members:
    - "conditional random fields as recurrent neural networks"
    - "human pose estimation with iterative error feedback"
    - "joint training of a convolutional network and a graphical model for human pose estimation"
    - "deep structured output learning for unconstrained text recognition"
    - "articulated pose estimation by a graphical model with image dependent pairwise relations"
    - "heterogeneous multi task learning for human pose estimation with deep convolutional neural network"
    - "cnn features off the shelf an astounding baseline for recognition"
    - "deeppose human pose estimation via deep neural networks"

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)