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: "2017 robotic instrument segmentation challenge"
  Abstract: In mainstream computer vision and machine learning, public datasets such as ImageNet, COCO and KITTI have helped drive enormous improvements by enabling researchers to understand the strengths and limitations of different algorithms via performance comparison. However, this type of approach has had limited translation to problems in robotic assisted surgery as this field has never established the same level of common datasets and benchmarking methods. In 2015 a sub-challenge was introduced at the EndoVis workshop where a set of robotic images were provided with automatically generated annotations from robot forward kinematics. However, there were issues with this dataset due to the limited background variation, lack of complex motion and inaccuracies in the annotation. In this work we present the results of the 2017 challenge on robotic instrument segmentation which involved 10 teams participating in binary, parts and type based segmentation of articulated da Vinci robotic instruments.

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"
    - "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"
    - "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"
    - "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"
    - "sem gan semantically consistent image to image translation"

Connected through hyperedge HE-3 (induced by source paper "2018 robotic scene segmentation challenge"):
  with members:
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "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 "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"
    - "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"
    - "pyramid attention network for semantic segmentation"

Connected through hyperedge HE-5 (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"
    - "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"
    - "salgan visual saliency prediction with generative adversarial networks"

Connected through hyperedge HE-6 (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"
    - "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"
    - "toolnet holistically nested real time segmentation of robotic surgical tools"

Connected through hyperedge HE-7 (induced by source paper "cadis cataract dataset for image segmentation"):
  with members:
    - "improving surgical training phantoms by hyperrealism deep unpaired image to image translation from real surgeries"
    - "unified perceptual parsing for scene understanding"
    - "encoder decoder with atrous separable convolution for semantic image segmentation"
    - "deepphase surgical phase recognition in cataracts videos"
    - "surgical data science for next generation interventions"
    - "rethinking atrous convolution for semantic image segmentation"
    - "endonet a deep architecture for recognition tasks on laparoscopic videos"
    - "xception deep learning with depthwise separable convolutions"

Connected through hyperedge HE-8 (induced by source paper "rasnet segmentation for tracking surgical instruments in surgical videos using refined attention segmentation network"):
  with members:
    - "learning a discriminative feature network for semantic segmentation"
    - "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
    - "tool detection and operative skill assessment in surgical videos using region based convolutional neural networks"
    - "pyramid attention network for semantic segmentation"
    - "squeeze and excitation networks"
    - "concurrent segmentation and localization for tracking of surgical instruments"
    - "u net convolutional networks for biomedical image segmentation"
    - "deep residual learning for image recognition"

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)