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: "multiview two task recursive attention model for left atrium and atrial scars segmentation"
  Abstract: Late Gadolinium Enhanced Cardiac MRI (LGE-CMRI) for detecting atrial scars in atrial fibrillation (AF) patients has recently emerged as a promising technique to stratify patients, guide ablation therapy and predict treatment success. Visualisation and quantification of scar tissues require a segmentation of both the left atrium (LA) and the high intensity scar regions from LGE-CMRI images. These two segmentation tasks are challenging due to the cancelling of healthy tissue signal, low signal-to-noise ratio and often limited image quality in these patients. Most approaches require manual supervision and/or a second bright-blood MRI acquisition for anatomical segmentation. Segmenting both the LA anatomy and the scar tissues automatically from a single LGE-CMRI acquisition is highly in demand. In this study, we proposed a novel fully automated multiview two-task (MVTT) recursive attention model working directly on LGE-CMRI images that combines a sequential learning and a dilated residual learning to segment the LA (including attached pulmonary veins) and delineate the atrial scars simultaneously via an innovative attention model. Compared to other state-of-the-art methods, the proposed MVTT achieves compelling improvement, enabling to generate a patient-specific anatomical and atrial scar assessment model.

Connected through hyperedge HE-1 (induced by source paper "incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video"):
  with members:
    - "u netplus a modified encoder decoder u net architecture for semantic and instance segmentation of surgical instrument"
    - "2017 robotic instrument segmentation challenge"
    - "cfcm segmentation via coarse to fine context memory"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "attention u net learning where to look for the pancreas"
    - "concurrent segmentation and localization for tracking of surgical instruments"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"
    - "toolnet holistically nested real time segmentation of robotic surgical tools"

Connected through hyperedge HE-2 (induced by source paper "atrial scar quantification via multi scale cnn in the graph cuts framework"):
  with members:
    - "fully automatic segmentation and objective assessment of atrial scars for long standing persistent atrial fibrillation patients using late gadolinium enhanced mri"
    - "atrial fibrosis quantification based on maximum likelihood estimator of multivariate images"
    - "scargan chained generative adversarial networks to simulate pathological tissue on cardiovascular mr scans"
    - "efficient multi scale 3d cnn with fully connected crf for accurate brain lesion segmentation"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "semi supervised classification with graph convolutional networks"
    - "pointnet deep learning on point sets for 3d classification and segmentation"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "discriminative consistent domain generation for semi supervised learning"):
  with members:
    - "learning to adapt structured output space for semantic segmentation"
    - "fully automatic segmentation and objective assessment of atrial scars for long standing persistent atrial fibrillation patients using late gadolinium enhanced mri"
    - "domain invariant adversarial learning for unsupervised domain adaption"
    - "an adversarial regularisation for semi supervised training of structured output neural networks"
    - "3d densely convolutional networks for volumetric segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-4 (induced by source paper "pyramid network with online hard example mining for accurate left atrium segmentation"):
  with members:
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "cardiacnet segmentation of left atrium and proximal pulmonary veins from mri using multi view cnn"
    - "rethinking atrous convolution for semantic image segmentation"
    - "training region based object detectors with online hard example mining"
    - "pyramid scene parsing network"
    - "segnet a deep convolutional encoder decoder architecture for image segmentation"

Connected through hyperedge HE-5 (induced by source paper "combating uncertainty with novel losses for automatic left atrium segmentation"):
  with members:
    - "cardiacnet segmentation of left atrium and proximal pulmonary veins from mri using multi view cnn"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "faster r cnn towards real time object detection with region proposal networks"
    - "learning spatiotemporal features with 3d convolutional 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)