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: "active learning and proofreading for delineation of curvilinear structures"
  Abstract: Many state-of-the-art delineation methods rely on supervised machine learning algorithms. As a result, they require manually annotated training data, which is tedious to obtain. Furthermore, even minor classification errors may significantly affect the topology of the final result. In this paper we propose a generic approach to addressing both of these problems by taking into account the influence of a potential misclassification on the resulting delineation. In an Active Learning context, we identify parts of linear structures that should be annotated first in order to train a classifier effectively. In a proofreading context, we similarly find regions of the resulting reconstruction that should be verified in priority to obtain a nearly-perfect result. In both cases, by focusing the attention of the human expert on potential classification mistakes which are the most critical parts of the delineation, we reduce the amount of required supervision. We demonstrate the effectiveness of our approach on microscopy images depicting blood vessels and neurons.

Connected through hyperedge HE-1 (induced by source paper "vm net mesh modeling to assist segmentation in volumetric data"):
  with members:
    - "neuralsampler euclidean point cloud auto encoder and sampler"
    - "data augmentation using learned transformations for one shot medical image segmentation"
    - "a large annotated medical image dataset for the development and evaluation of segmentation algorithms"
    - "pixel2mesh multi view 3d mesh generation via deformation"
    - "deep mesh reconstruction from single rgb images via topology modification networks"
    - "deep sequential segmentation of organs in volumetric medical scans"
    - "pixel2mesh generating 3d mesh models from single rgb images"
    - "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation"

Connected through hyperedge HE-2 (induced by source paper "joint segmentation and path classification of curvilinear structures"):
  with members:
    - "el gan embedding loss driven generative adversarial networks for lane detection"
    - "roadtracer automatic extraction of road networks from aerial images"
    - "iterative deep learning for network topology extraction"
    - "beyond the pixel wise loss for topology aware delineation"
    - "deep retinal image understanding"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "holistically nested edge detection"
    - "u net convolutional networks for biomedical image segmentation"

Connected through hyperedge HE-3 (induced by source paper "aft integrating active learning and transfer learning to reduce annotation efforts"):
  with members:
    - "convolutional neural networks for medical image analysis full training or fine tuning"
    - "learning active learning from data"
    - "suggestive annotation a deep active learning framework for biomedical image segmentation"
    - "a survey on deep learning in medical image analysis"
    - "comparison of machine learning methods for classifying mediastinal lymph node metastasis of non small cell lung cancer from 18f fdg pet ct images"
    - "densely connected convolutional networks"
    - "deep residual learning for image recognition"
    - "going deeper with convolutions"

Connected through hyperedge HE-4 (induced by source paper "tracing in 2d to reduce the annotation effort for 3d deep delineation"):
  with members:
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "u net convolutional networks for biomedical image segmentation"
    - "n 4 fields neural network nearest neighbor fields for image transforms"

Connected through hyperedge HE-5 (induced by source paper "beyond the pixel wise loss for topology aware delineation"):
  with members:
    - "multi stage multi recursive input fully convolutional networks for neuronal boundary detection"
    - "deep retinal image understanding"
    - "stacked hourglass networks for human pose estimation"
    - "3d u net learning dense volumetric segmentation from sparse annotation"
    - "flood filling networks"
    - "holistically nested edge detection"
    - "u net convolutional networks for biomedical image segmentation"
    - "densecap fully convolutional localization networks for dense captioning"

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)