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 "fully automated pancreas segmentation with two stage 3d convolutional neural networks"):
  Abstract: Due to the fact that pancreas is an abdominal organ with very large variations in shape and size, automatic and accurate pancreas segmentation can be challenging for medical image analysis. In this work, we proposed a fully automated two stage framework for pancreas segmentation based on convolutional neural networks (CNN). In the first stage, a U-Net is trained for the down-sampled 3D volume segmentation. Then a candidate region covering the pancreas is extracted from the estimated labels. Motivated by the superior performance reported by renowned region based CNN, in the second stage, another 3D U-Net is trained on the candidate region generated in the first stage. We evaluated the performance of the proposed method on the NIH computed tomography (CT) dataset, and verified its superiority over other state-of-the-art 2D and 3D approaches for pancreas segmentation in terms of dice-sorensen coefficient (DSC) accuracy in testing. The mean DSC of the proposed method is 85.99%.

Includes members:
  Member "probability map guided bi directional recurrent unet for pancreas segmentation":
    also connected through other hyperedges:
      (none)
  Member "attention u net learning where to look for the pancreas":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "super resolution using segmentation prior self attention generative adversarial network")
  Member "bridging the gap between 2d and 3d organ segmentation with volumetric fusion net":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "c2fnas coarse to fine neural architecture search for 3d medical image segmentation")
  Member "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "elixirnet relation aware network architecture adaptation for medical lesion detection")
  Member "a 3d coarse to fine framework for volumetric medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "object contextual representations for semantic segmentation")
  Member "spatial aggregation of holistically nested convolutional neural networks for automated pancreas localization and segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "mcmc guided cnn training and segmentation for pancreas extraction")
  Member "spatial aggregation of holistically nested networks for automated pancreas segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mcmc guided cnn training and segmentation for pancreas extraction")
  Member "a fixed point model for pancreas segmentation in abdominal ct scans":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "prior aware neural network for partially supervised multi organ segmentation")

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)