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: "toolnet holistically nested real time segmentation of robotic surgical tools"
  Abstract: Real-time tool segmentation from endoscopic videos is an essential part of many computer-assisted robotic surgical systems and of critical importance in robotic surgical data science. We propose two novel deep learning architectures for automatic segmentation of non-rigid surgical instruments. Both methods take advantage of automated deep-learning-based multi-scale feature extraction while trying to maintain an accurate segmentation quality at all resolutions. The two proposed methods encode the multi-scale constraint inside the network architecture. The first proposed architecture enforces it by cascaded aggregation of predictions and the second proposed network does it by means of a holistically-nested architecture where the loss at each scale is taken into account for the optimization process. As the proposed methods are for real-time semantic labeling, both present a reduced number of parameters. We propose the use of parametric rectified linear units for semantic labeling in these small architectures to increase the regularization of the network while maintaining the segmentation accuracy. We compare the proposed architectures against state-of-the-art fully convolutional networks. We validate our methods using existing benchmark datasets, including ex vivo cases with phantom tissue and different robotic surgical instruments present in the scene. Our results show a statistically significant improved Dice Similarity Coefficient over previous instrument segmentation methods. We analyze our design choices and discuss the key drivers for improving accuracy.

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"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"

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"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"

Connected through hyperedge HE-3 (induced by source paper "attention guided lightweight network for real time segmentation of robotic surgical instruments"):
  with members:
    - "rasnet segmentation for tracking surgical instruments in surgical videos using refined attention segmentation network"
    - "2017 robotic instrument segmentation challenge"
    - "mobilenetv2 inverted residuals and linear bottlenecks"
    - "shufflenet v2 practical guidelines for efficient cnn architecture design"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "light weight refinenet for real time semantic segmentation"
    - "real time joint semantic segmentation and depth estimation using asymmetric annotations"
    - "dual attention network for scene segmentation"

Connected through hyperedge HE-4 (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"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "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"

Connected through hyperedge HE-5 (induced by source paper "2017 robotic instrument segmentation challenge"):
  with members:
    - "cfcm segmentation via coarse to fine context memory"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "simultaneous recognition and pose estimation of instruments in minimally invasive surgery"
    - "concurrent segmentation and localization for tracking of surgical instruments"
    - "deep residual learning for instrument segmentation in robotic surgery"
    - "deeper depth prediction with fully convolutional residual networks"
    - "holistically nested edge detection"

Connected through hyperedge HE-6 (induced by source paper "deep attention based semi supervised 2d pose estimation for surgical instruments"):
  with members:
    - "billion scale semi supervised learning for image classification"
    - "group normalization"
    - "modeling visual context is key to augmenting object detection datasets"
    - "recent advances in autoencoder based representation learning"
    - "realistic evaluation of deep semi supervised learning algorithms"
    - "random erasing data augmentation"
    - "mean teachers are better role models weight averaged consistency targets improve semi supervised deep learning results"
    - "concurrent segmentation and localization for tracking of surgical instruments"

Connected through hyperedge HE-7 (induced by source paper "learning where to look while tracking instruments in robot assisted surgery"):
  with members:
    - "decoders matter for semantic segmentation data dependent decoding enables flexible feature aggregation"
    - "2017 robotic instrument segmentation challenge"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "concurrent spatial and channel squeeze excitation in fully convolutional networks"
    - "real time joint semantic segmentation and depth estimation using asymmetric annotations"
    - "blitznet a real time deep network for scene understanding"
    - "large kernel matters improve semantic segmentation by global convolutional network"
    - "linknet exploiting encoder representations for efficient semantic segmentation"

Connected through hyperedge HE-8 (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"
    - "multiview two task recursive attention model for left atrium and atrial scars segmentation"
    - "concurrent segmentation and localization for tracking of surgical instruments"
    - "unflow unsupervised learning of optical flow with a bidirectional census loss"

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)