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: "mr based synthetic ct generation using a deep convolutional neural network method"
  Abstract: Purpose#R##N#Interests have been rapidly growing in the field of radiotherapy to replace CT with magnetic resonance imaging (MRI), due to superior soft tissue contrast offered by MRI and the desire to reduce unnecessary radiation dose. MR-only radiotherapy also simplifies clinical workflow and avoids uncertainties in aligning MR with CT. Methods, however, are needed to derive CT-equivalent representations, often known as synthetic CT (sCT), from patient MR images for dose calculation and DRR-based patient positioning. Synthetic CT estimation is also important for PET attenuation correction in hybrid PET-MR systems. We propose in this work a novel deep convolutional neural network (DCNN) method for sCT generation and evaluate its performance on a set of brain tumor patient images.#R##N##R##N#Methods#R##N#The proposed method builds upon recent developments of deep learning and convolutional neural networks in the computer vision literature. The proposed DCNN model has 27 convolutional layers interleaved with pooling and unpooling layers and 35 million free parameters, which can be trained to learn a direct end-to-end mapping from MR images to their corresponding CTs. Training such a large model on our limited data is made possible through the principle of transfer learning and by initializing model weights from a pretrained model. Eighteen brain tumor patients with both CT and T1-weighted MR images are used as experimental data and a sixfold cross-validation study is performed. Each sCT generated is compared against the real CT image of the same patient on a voxel-by-voxel basis. Comparison is also made with respect to an atlas-based approach that involves deformable atlas registration and patch-based atlas fusion.#R##N##R##N#Results#R##N#The proposed DCNN method produced a mean absolute error (MAE) below 85 HU for 13 of the 18 test subjects. The overall average MAE was 84.8 ± 17.3 HU for all subjects, which was found to be significantly better than the average MAE of 94.5 ± 17.8 HU for the atlas-based method. The DCNN method also provided significantly better accuracy when being evaluated using two other metrics: the mean squared error (188.6 ± 33.7 versus 198.3 ± 33.0) and the Pearson correlation coefficient(0.906 ± 0.03 versus 0.896 ± 0.03). Although training a DCNN model can be slow, training only need be done once. Applying a trained model to generate a complete sCT volume for each new patient MR image only took 9 s, which was much faster than the atlas-based approach.#R##N##R##N#Conclusions#R##N#A DCNN model method was developed, and shown to be able to produce highly accurate sCT estimations from conventional, single-sequence MR images in near real time. Quantitative results also showed that the proposed method competed favorably with an atlas-based method, in terms of both accuracy and computation speed at test time. Further validation on dose computation accuracy and on a larger patient cohort is warranted. Extensions of the method are also possible to further improve accuracy or to handle multi-sequence MR images.

Connected through hyperedge HE-1 (induced by source paper "multi organ segmentation over partially labeled datasets with multi scale feature abstraction"):
  with members:
    - "boundary weighted domain adaptive neural network for prostate mr image segmentation"
    - "learning a deep convnet for multi label classification with partial labels"
    - "use net incorporating squeeze and excitation blocks into u net for prostate zonal segmentation of multi institutional mri datasets"
    - "feature fusion encoder decoder network for automatic liver lesion segmentation"
    - "ce net context encoder network for 2d medical image segmentation"
    - "the liver tumor segmentation benchmark lits"
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "training of convolutional networks on multiple heterogeneous datasets for street scene semantic segmentation"

Connected through hyperedge HE-2 (induced by source paper "recurrent convolutional neural networks for mandible segmentation from computed tomography"):
  with members:
    - "anatomynet deep learning for fast and fully automated whole volume segmentation of head and neck anatomy"
    - "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"
    - "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-3 (induced by source paper "kernel of cyclegan as a principle homogeneous space"):
  with members:
    - "miso mutual information loss with stochastic style representations for multimodal image to image translation"
    - "unsupervised image to image translation networks"
    - "deep mr to ct synthesis using unpaired data"
    - "density estimation using real nvp"
    - "a neural algorithm of artistic style"

Connected through hyperedge HE-4 (induced by source paper "med3d transfer learning for 3d medical image analysis"):
  with members:
    - "evaluation of algorithms for multi modality whole heart segmentation an open access grand challenge"
    - "rethinking imagenet pre training"
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "understanding the mechanisms of deep transfer learning for medical images"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-5 (induced by source paper "feature fusion encoder decoder network for automatic liver lesion segmentation"):
  with members:
    - "exfuse enhancing feature fusion for semantic segmentation"
    - "detection aided liver lesion segmentation using deep learning"
    - "squeeze and excitation networks"
    - "automatic liver lesion detection using cascaded deep residual networks"
    - "refinenet multi path refinement networks for high resolution semantic segmentation"
    - "real time single image and video super resolution using an efficient sub pixel convolutional neural network"
    - "3d deeply supervised network for automatic liver segmentation from ct volumes"
    - "automatic liver and lesion segmentation in ct using cascaded fully convolutional neural networks and 3d conditional random fields"

Connected through hyperedge HE-6 (induced by source paper "liver lesion segmentation with slice wise 2d tiramisu and tversky loss function"):
  with members:
    - "the liver tumor segmentation benchmark lits"
    - "automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks"
    - "neural network based automatic liver tumor segmentation with random forest based candidate filtering"
    - "tversky loss function for image segmentation using 3d fully convolutional deep networks"
    - "the one hundred layers tiramisu fully convolutional densenets for semantic segmentation"
    - "densely connected convolutional networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "delving deep into rectifiers surpassing human level performance on imagenet classification"

Connected through hyperedge HE-7 (induced by source paper "snu_ids at semeval 2019 task 3 addressing training test class distribution mismatch in conversational classification"):
  with members:
    - "deep contextualized word representations"
    - "a systematic study of the class imbalance problem in convolutional neural networks"
    - "focal loss for dense object detection"
    - "shortcut stacked sentence encoders for multi domain inference"
    - "distance based self attention network for natural language inference"
    - "disan directional self attention network for rnn cnn free language understanding"
    - "emoji2vec learning emoji representations from their description"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "ct synthesis from mr images for orthopedic applications in the lower arm using a conditional generative adversarial network"):
  with members:
    - "deep mr to ct synthesis using unpaired data"
    - "medical image synthesis with context aware generative adversarial networks"
    - "image to image translation with conditional adversarial networks"
    - "u net convolutional networks for biomedical image segmentation"
    - "adam a method for stochastic optimization"

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)