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: "automatic instrument segmentation in robot assisted surgery using deep learning"
  Abstract: Semantic segmentation of robotic instruments is an important problem for the robot-assisted surgery. One of the main challenges is to correctly detect an instrument's position for the tracking and pose estimation in the vicinity of surgical scenes. Accurate pixel-wise instrument segmentation is needed to address this challenge. In this paper we describe our deep learning-based approach for robotic instrument segmentation. Our approach demonstrates an improvement over the state-of-the-art results using several deep neural network architectures. It addressed the binary segmentation problem, where every pixel in an image is labeled as an instrument or background from the surgery video feed. In addition, we address a multi-class segmentation problem, in which we distinguish between different instruments or different parts of an instrument from the background. In this setting, our approach outperforms other methods for automatic instrument segmentation thereby providing state-of-the-art results for these problems. The source code for our solution is made publicly available at https://github.com/ternaus/robot-surgery-segmentation.

Connected through hyperedge HE-1 (induced by source paper "ap mtl attention pruned multi task learning model for real time instrument detection and segmentation in robot assisted surgery"):
  with members:
    - "real time joint object detection and semantic segmentation network for automated driving"
    - "learning where to look while tracking instruments in robot assisted surgery"
    - "2017 robotic instrument segmentation challenge"
    - "visualizing uncertainty and saliency maps of deep convolutional neural networks for medical imaging applications"
    - "bisenet bilateral segmentation network for real time semantic segmentation"
    - "yolov3 an incremental improvement"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"

Connected through hyperedge HE-2 (induced by source paper "lc gan image to image translation based on generative adversarial network for endoscopic images"):
  with members:
    - "2018 robotic scene segmentation challenge"
    - "towards better surgical instrument segmentation in endoscopic vision multi angle feature aggregation and contour supervision"
    - "attention guided lightweight network for real time segmentation of robotic surgical instruments"
    - "learning where to look while tracking instruments in robot assisted surgery"
    - "2017 robotic instrument segmentation challenge"
    - "not so supervised a survey of semi supervised multi instance and transfer learning in medical image analysis"
    - "multimodal unsupervised image to image translation"
    - "sem gan semantically consistent image to image translation"

Connected through hyperedge HE-3 (induced by source paper "2018 robotic scene segmentation challenge"):
  with members:
    - "2017 robotic instrument segmentation challenge"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "in place activated batchnorm for memory optimized training of dnns"
    - "aggregated residual transformations for deep neural networks"
    - "image to image translation with conditional adversarial networks"
    - "xception deep learning with depthwise separable convolutions"
    - "pyramid scene parsing network"

Connected through hyperedge HE-4 (induced by source paper "albumentations fast and flexible image augmentations"):
  with members:
    - "camera model identification using convolutional neural networks"
    - "paediatric bone age assessment using deep convolutional neural networks"
    - "angiodysplasia detection and localization using deep convolutional neural networks"
    - "fully convolutional network for automatic road extraction from satellite imagery"
    - "autoaugment learning augmentation policies from data"
    - "feature pyramid network for multi class land segmentation"
    - "smart augmentation learning an optimal data augmentation strategy"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-5 (induced by source paper "an abstraction model for semantic segmentation algorithms"):
  with members:
    - "semantic segmentation of pathological lung tissue with dilated fully convolutional networks"
    - "aerial lanenet lane marking semantic segmentation in aerial imagery using wavelet enhanced cost sensitive symmetric fully convolutional neural networks"
    - "convolutional crfs for semantic segmentation"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "pyramid attention network for semantic segmentation"
    - "future semantic segmentation with convolutional lstm"
    - "deepunet a deep fully convolutional network for pixel level sea land segmentation"
    - "the effectiveness of data augmentation in image classification using deep learning"

Connected through hyperedge HE-6 (induced by source paper "maskplus improving mask generation for instance segmentation"):
  with members:
    - "hybrid task cascade for instance segmentation"
    - "path aggregation network for instance segmentation"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "recurrent pixel embedding for instance grouping"
    - "cascade r cnn delving into high quality object detection"
    - "gland instance segmentation using deep multichannel neural networks"
    - "masklab instance segmentation by refining object detection with semantic and direction features"
    - "semantic instance segmentation via deep metric learning"

Connected through hyperedge HE-7 (induced by source paper "joint iris segmentation and localization using deep multi task learning framework"):
  with members:
    - "deeply supervised salient object detection with short connections"
    - "bam bottleneck attention module"
    - "learning a discriminative feature network for semantic segmentation"
    - "a benchmark for iris location and a deep learning detector evaluation"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "learning to find eye region landmarks for remote gaze estimation in unconstrained settings"
    - "cbam convolutional block attention module"
    - "focal loss for dense object detection"

Connected through hyperedge HE-8 (induced by source paper "u netplus a modified encoder decoder u net architecture for semantic and instance segmentation of surgical instrument"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "rethinking imagenet pre training"
    - "do better imagenet models transfer better"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "learning to see in the dark"
    - "super resolution with deep adaptive image resampling"
    - "concurrent segmentation and localization for tracking of surgical instruments"
    - "deep residual learning for instrument segmentation in robotic surgery"

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)