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: "concurrent segmentation and localization for tracking of surgical instruments"
  Abstract: Real-time instrument tracking is a crucial requirement for various computer-assisted interventions. In order to overcome problems such as specular reflections and motion blur, we propose a novel method that takes advantage of the interdependency between localization and segmentation of the surgical tool. In particular, we reformulate the 2D instrument pose estimation as heatmap regression and thereby enable a concurrent, robust and near real-time regression of both tasks via deep learning. As demonstrated by our experimental results, this modeling leads to a significantly improved performance than directly regressing the tool position and allows our method to outperform the state of the art on a Retinal Microsurgery benchmark and the MICCAI EndoVis Challenge 2015.

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 "multi task recurrent convolutional network with correlation loss for surgical video analysis"):
  with members:
    - "incorporating temporal prior from motion flow for instrument segmentation in minimally invasive surgery video"
    - "deep reinforcement learning for surgical gesture segmentation and classification"
    - "uncertainty in multitask learning joint representations for probabilistic mr only radiotherapy planning"
    - "less is more surgical phase recognition with less annotations through self supervised pre training of cnn lstm networks"
    - "deepphase surgical phase recognition in cataracts videos"
    - "multi task learning of pairwise sequence classification tasks over disparate label spaces"
    - "automated pulmonary nodule detection via 3d convnets with online sample filtering and hybrid loss residual learning"
    - "fine grained recognition in the wild a multi task domain adaptation approach"

Connected through hyperedge HE-3 (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"
    - "deep residual learning for instrument segmentation in robotic surgery"

Connected through hyperedge HE-4 (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"
    - "deep residual learning for instrument segmentation in robotic surgery"
    - "toolnet holistically nested real time segmentation of robotic surgical tools"
    - "deeper depth prediction with fully convolutional residual networks"
    - "holistically nested edge detection"

Connected through hyperedge HE-5 (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"
    - "toolnet holistically nested real time segmentation of robotic surgical tools"

Connected through hyperedge HE-6 (induced by source paper "video based surgical skill assessment using 3d convolutional neural networks"):
  with members:
    - "deep learning with convolutional neural network for objective skill evaluation in robot assisted surgery"
    - "evaluating surgical skills from kinematic data using convolutional neural networks"
    - "tool detection and operative skill assessment in surgical videos using region based convolutional neural networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "video and accelerometer based motion analysis for automated surgical skills assessment"
    - "temporal segment networks for action recognition in videos"
    - "automated surgical skill assessment in rmis training"
    - "who s better who s best pairwise deep ranking for skill determination"

Connected through hyperedge HE-7 (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"
    - "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-8 (induced by source paper "self supervised surgical tool segmentation using kinematic information"):
  with members:
    - "weakly supervised learning for tool localization in laparoscopic videos"
    - "exploiting the potential of unlabeled endoscopic video data with self supervised learning"
    - "time contrastive networks self supervised learning from video"
    - "deep residual learning for instrument segmentation in robotic surgery"
    - "toolnet holistically nested real time segmentation of robotic surgical tools"
    - "learning hand eye coordination for robotic grasping with deep learning and large scale data collection"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "fully convolutional networks for semantic 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)