human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "segmentation of roots in soil with u net"):
  Abstract: Plant root research can provide a way to attain stress-tolerant crops that produce greater yield in a diverse array of conditions. Phenotyping roots in soil is often challenging due to the roots being difficult to access and the use of time consuming manual methods. Rhizotrons allow visual inspection of root growth through transparent surfaces. Agronomists currently manually label photographs of roots obtained from rhizotrons using a line-intersect method to obtain root length density and rooting depth measurements which are essential for their experiments. We investigate the effectiveness of an automated image segmentation method based on the U-Net Convolutional Neural Network (CNN) architecture to enable such measurements. We design a data-set of 50 annotated Chicory (Cichorium intybus L.) root images which we use to train, validate and test the system and compare against a baseline built using the Frangi vesselness filter. We obtain metrics using manual annotations and line-intersect counts. Our results on the held out data show our proposed automated segmentation system to be a viable solution for detecting and quantifying roots. We evaluate our system using 867 images for which we have obtained line-intersect counts, attaining a Spearman rank correlation of 0.9748 and an $r^2$ of 0.9217. We also achieve an $F_1$ of 0.7 when comparing the automated segmentation to the manual annotations, with our automated segmentation system producing segmentations with higher quality than the manual annotations for large portions of the image.

Includes members:
  Member "squeeze excite guided few shot segmentation of volumetric images":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "learn to segment organs with a few bounding boxes")
  Member "flow based self supervised pixel embedding for image segmentation":
    also connected through other hyperedges:
      (none)
  Member "on compressing u net using knowledge distillation":
    also connected through other hyperedges:
      (none)
  Member "understanding the disharmony between dropout and batch normalization by variance shift":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "salsanext fast semantic segmentation of lidar point clouds for autonomous driving")
  Member "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "ap mtl attention pruned multi task learning model for real time instrument detection and segmentation in robot assisted surgery")
  Member "fully convolutional multi scale residual densenets for cardiac segmentation and automated cardiac diagnosis using ensemble of classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "rethinking fully convolutional networks for the analysis of photoluminescence wafer images")
  Member "a systematic study of the class imbalance problem in convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "identifying and compensating for feature deviation in imbalanced deep learning")
  Member "cnn based segmentation of medical imaging data":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "low memory neural network training a technical report")

Question: Please tell me which class the source paper 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)