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: "deep residual learning for instrument segmentation in robotic surgery"
  Abstract: Detection, tracking, and pose estimation of surgical instruments are crucial tasks for computer assistance during minimally invasive robotic surgery. In the majority of cases, the first step is the automatic segmentation of surgical tools. Prior work has focused on binary segmentation, where the objective is to label every pixel in an image as tool or background. We improve upon previous work in two major ways. First, we leverage recent techniques such as deep residual learning and dilated convolutions to advance binary-segmentation performance. Second, we extend the approach to multi-class segmentation, which lets us segment different parts of the tool, in addition to background. We demonstrate the performance of this method on the MICCAI Endoscopic Vision Challenge Robotic Instruments dataset.

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 "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-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"
    - "concurrent segmentation and localization for tracking of surgical instruments"

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"
    - "concurrent segmentation and localization for tracking of surgical instruments"
    - "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 "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"
    - "concurrent segmentation and localization for tracking of surgical instruments"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "automatic instrument segmentation in robot assisted surgery using deep learning"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "fixed point convolutional neural network for real time video processing in fpga"
    - "deep convolutional neural networks for breast cancer histology image analysis"
    - "paediatric bone age assessment using deep convolutional neural networks"
    - "angiodysplasia detection and localization using deep convolutional neural networks"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "automatic knee osteoarthritis diagnosis from plain radiographs a deep learning based approach"

Connected through hyperedge HE-7 (induced by source paper "cfcm segmentation via coarse to fine context memory"):
  with members:
    - "concurrent segmentation and localization for tracking of surgical instruments"
    - "a survey on deep learning in medical image analysis"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "v net fully convolutional neural networks for volumetric medical image segmentation"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"
    - "convolutional lstm network a machine learning approach for precipitation nowcasting"

Connected through hyperedge HE-8 (induced by source paper "comparative evaluation of instrument segmentation and tracking methods in minimally invasive surgery"):
  with members:
    - "very deep convolutional networks for large scale image recognition"
    - "caffe convolutional architecture for fast feature embedding"
    - "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)