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: "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation"
  Abstract: Pixel-wise image segmentation is demanding task in computer vision. Classical U-Net architectures composed of encoders and decoders are very popular for segmentation of medical images, satellite images etc. Typically, neural network initialized with weights from a network pre-trained on a large data set like ImageNet shows better performance than those trained from scratch on a small dataset. In some practical applications, particularly in medicine and traffic safety, the accuracy of the models is of utmost importance. In this paper, we demonstrate how the U-Net type architecture can be improved by the use of the pre-trained encoder. Our code and corresponding pre-trained weights are publicly available at this https URL We compare three weight initialization schemes: LeCun uniform, the encoder with weights from VGG11 and full network trained on the Carvana dataset. This network architecture was a part of the winning solution (1st out of 735) in the Kaggle: Carvana Image Masking Challenge.

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

Connected through hyperedge HE-3 (induced by source paper "abdominal multi organ segmentation with cascaded convolutional and adversarial deep networks"):
  with members:
    - "abdominal multi organ segmentation with organ attention networks and statistical fusion"
    - "breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network"
    - "a multi scale pyramid of 3d fully convolutional networks for abdominal multi organ segmentation"
    - "unet a nested u net architecture for medical image segmentation"
    - "ternausnetv2 fully convolutional network for instance segmentation"
    - "auto context convolutional neural network auto net for brain extraction in magnetic resonance imaging"
    - "hierarchical 3d fully convolutional networks for multi organ segmentation"
    - "a survey on deep learning in medical image analysis"

Connected through hyperedge HE-4 (induced by source paper "deep learning approach to diabetic retinopathy detection"):
  with members:
    - "albumentations fast and flexible image augmentations"
    - "on the variance of the adaptive learning rate and beyond"
    - "transfer learning based detection of diabetic retinopathy from small dataset"
    - "efficientnet rethinking model scaling for convolutional neural networks"
    - "on first order meta learning algorithms"
    - "deep learning based computer aided diagnosis systems for diabetic retinopathy a survey"
    - "grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy"
    - "focal loss for dense object detection"

Connected through hyperedge HE-5 (induced by source paper "data free adversarial perturbations for practical black box attack"):
  with members:
    - "enhancing adversarial example transferability with an intermediate level attack"
    - "generalizable data free objective for crafting universal adversarial perturbations"
    - "universal adversarial perturbations"
    - "adversarial examples in the physical world"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"
    - "very deep convolutional networks for large scale image recognition"
    - "intriguing properties of neural networks"

Connected through hyperedge HE-6 (induced by source paper "transfer learning in visual and relational reasoning"):
  with members:
    - "leveraging medical visual question answering with supporting facts"
    - "learning by abstraction the neural state machine"
    - "trends in integration of vision and language research a survey of tasks datasets and methods"
    - "huggingface s transformers state of the art natural language processing"
    - "do better imagenet models transfer better"
    - "transparency by design closing the gap between performance and interpretability in visual reasoning"
    - "taskonomy disentangling task transfer learning"
    - "object based reasoning in vqa"

Connected through hyperedge HE-7 (induced by source paper "colormapgan unsupervised domain adaptation for semantic segmentation using color mapping generative adversarial networks"):
  with members:
    - "unsupervised domain adaptation using generative adversarial networks for semantic segmentation of aerial images"
    - "active multi kernel domain adaptation for hyperspectral image classification"
    - "penalizing top performers conservative loss for semantic segmentation adaptation"
    - "learning to adapt structured output space for semantic segmentation"
    - "incremental learning for semantic segmentation of large scale remote sensing data"
    - "multimodal unsupervised image to image translation"
    - "a domain agnostic normalization layer for unsupervised adversarial domain adaptation"
    - "ternausnetv2 fully convolutional network for instance segmentation"

Connected through hyperedge HE-8 (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"
    - "automatic instrument segmentation in robot assisted surgery using deep learning"
    - "learning to find eye region landmarks for remote gaze estimation in unconstrained settings"
    - "cbam convolutional block attention module"
    - "focal loss for dense object detection"

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)