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: "anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy"
  Abstract: Methods: Our deep learning model, called AnatomyNet, segments OARs from head and neck CT images in an end-to-end fashion, receiving whole-volume HaN CT images as input and generating masks of all OARs of interest in one shot. AnatomyNet is built upon the popular 3D U-net architecture, but extends it in three important ways: 1) a new encoding scheme to allow auto-segmentation on whole-volume CT images instead of local patches or subsets of slices, 2) incorporating 3D squeeze-and-excitation residual blocks in encoding layers for better feature representation, and 3) a new loss function combining Dice scores and focal loss to facilitate the training of the neural model. These features are designed to address two main challenges in deep-learning-based HaN segmentation: a) segmenting small anatomies (i.e., optic chiasm and optic nerves) occupying only a few slices, and b) training with inconsistent data annotations with missing ground truth for some anatomical structures. #R##N#Results: We collected 261 HaN CT images to train AnatomyNet, and used MICCAI Head and Neck Auto Segmentation Challenge 2015 as a benchmark dataset to evaluate the performance of AnatomyNet. The objective is to segment nine anatomies: brain stem, chiasm, mandible, optic nerve left, optic nerve right, parotid gland left, parotid gland right, submandibular gland left, and submandibular gland right. Compared to previous state-of-the-art results from the MICCAI 2015 competition, AnatomyNet increases Dice similarity coefficient by 3.3% on average. AnatomyNet takes about 0.12 seconds to fully segment a head and neck CT image of dimension 178 x 302 x 225, significantly faster than previous methods. In addition, the model is able to process whole-volume CT images and delineate all OARs in one pass, requiring little pre- or post-processing. this https URL.

Connected through hyperedge HE-1 (induced by source paper "recurrent convolutional neural networks for mandible segmentation from computed tomography"):
  with members:
    - "combo loss handling input and output imbalance in multi organ segmentation"
    - "organ at risk segmentation in head and neck ct images by using a two stage segmentation framework based on 3d u net"
    - "attention u net learning where to look for the pancreas"
    - "ischemic stroke lesion segmentation in ct perfusion scans using pyramid pooling and focal loss"
    - "image segmentation algorithms overview"
    - "mr based synthetic ct generation using a deep convolutional neural network method"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "recent trends in deep learning based natural language processing"

Connected through hyperedge HE-2 (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"
    - "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"
    - "the microsoft 2017 conversational speech recognition system"

Connected through hyperedge HE-3 (induced by source paper "neurreg neural registration and its application to image segmentation"):
  with members:
    - "learning a probabilistic model for diffeomorphic registration"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "neural multi scale self supervised registration for echocardiogram dense tracking"
    - "voxelmorph a learning framework for deformable medical image registration"
    - "unsupervised learning for fast probabilistic diffeomorphic registration"
    - "airnet self supervised affine registration for 3d medical images using neural networks"
    - "an unsupervised learning model for deformable medical image registration"
    - "end to end unsupervised deformable image registration with a convolutional neural network"

Connected through hyperedge HE-4 (induced by source paper "mining domain knowledge improved framework towards automatically standardizing anatomical structure nomenclature in radiotherapy"):
  with members:
    - "res2net a new multi scale backbone architecture"
    - "benchmark analysis of representative deep neural network architectures"
    - "imagenet trained cnns are biased towards texture increasing shape bias improves accuracy and robustness"
    - "bagan data augmentation with balancing gan"
    - "autoaugment learning augmentation policies from data"
    - "non local neural networks"
    - "focal loss for dense object detection"
    - "attention is all you need"

Connected through hyperedge HE-5 (induced by source paper "shape aware organ segmentation by predicting signed distance maps"):
  with members:
    - "shape aware complementary task learning for multi organ segmentation"
    - "deepsdf learning continuous signed distance functions for shape representation"
    - "a distance map regularized cnn for cardiac cine mr image segmentation"
    - "distance transform regression for spatially aware deep semantic segmentation"
    - "group normalization"
    - "elastic boundary projection for 3d medical imaging segmentation"
    - "segan adversarial network with multi scale l 1 loss for medical image segmentation"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"

Connected through hyperedge HE-6 (induced by source paper "automatic pulmonary lobe segmentation using deep learning"):
  with members:
    - "automated pulmonary nodule detection using 3d deep convolutional neural networks"
    - "focal loss for dense object detection"
    - "deeplung 3d deep convolutional nets for automated pulmonary nodule detection and classification"
    - "validation comparison and combination of algorithms for automatic detection of pulmonary nodules in computed tomography images the luna16 challenge"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-7 (induced by source paper "a combined deep learning gradient boosting machine framework for fluid intelligence prediction"):
  with members:
    - "automated pulmonary nodule detection using 3d deep convolutional neural networks"
    - "brain tumor segmentation with deep neural networks"
    - "xgboost a scalable tree boosting system"

Connected through hyperedge HE-8 (induced by source paper "a novel focal tversky loss function with improved attention u net for lesion segmentation"):
  with members:
    - "the ham10000 dataset a large collection of multi source dermatoscopic images of common pigmented skin lesions"
    - "attention u net learning where to look for the pancreas"
    - "3d segmentation with exponential logarithmic loss for highly unbalanced object sizes"
    - "focal loss for dense object detection"
    - "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations"
    - "where s your focus personalized attention"
    - "skin lesion analysis toward melanoma detection a challenge at the 2017 international symposium on biomedical imaging isbi hosted by the international skin imaging collaboration isic"
    - "v net fully convolutional neural networks for volumetric medical image 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)