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 "mds net a model driven stack based fully convolutional network for pancreas segmentation"):
  Abstract: The irregular geometry and high inter-slice variability in computerized tomography (CT) scans of the human pancreas make an accurate segmentation of this crucial organ a challenging task for existing data-driven deep learning methods. To address this problem, we present a novel model-driven stack-based fully convolutional network with a bi-directional convolutional long short-term memory network for pancreas segmentation, termed MDS-Net. The MDS-Net's cost function includes data approximation term and prior knowledge regularization term combined with a stack scheme for capturing and fusing the two-dimensional (2D) and local three-dimensional (3D) context information. Specifically, 3D CT scans are divided into multiple stacks, and each multi-slice stack is used as a basic unit for network training and modeling of the local spatial context. To highlight the importance of single slices in segmentation, the inter-slice relationships in the stack data are also incorporated in the MDS-Net framework. For implementing this proposed model-driven method, we create a stack-based U-Net architecture and successfully derive its back-propagation procedure for end-to-end training. Furthermore, a bi-directional convolutional long short-term memory (BiCLSTM) network is utilized to integrate upper and lower slice information, thereby ensuring the consistency of adjacent CT slices and intra-stack. Finally, extensive quantitative assessments on the NIH Pancreas-CT dataset demonstrated higher pancreatic segmentation accuracy and reliability of MDS-Net compared to other state-of-the-art methods.

Includes members:
  Member "deep q learning driven ct pancreas segmentation with geometry aware u net":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi scale guided attention for medical image segmentation")
  Member "pancreas segmentation in ct and mri images via domain specific network designing and recurrent neural contextual learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "probability map guided bi directional recurrent unet for pancreas segmentation")
  Member "combining data driven and model driven methods for robust facial landmark detection":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "beyond face rotation global and local perception gan for photorealistic and identity preserving frontal view synthesis")
  Member "unet a nested u net architecture for medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi organ segmentation over partially labeled datasets with multi scale feature abstraction")
  Member "towards dense volumetric pancreas segmentation in ct using 3d fully convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "abdominal multi organ segmentation with organ attention networks and statistical fusion")
  Member "an application of cascaded 3d fully convolutional networks for medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "multi organ segmentation over partially labeled datasets with multi scale feature abstraction")
  Member "model driven deep learning for physical layer communications":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "dl based csi feedback and cooperative recovery in massive mimo")
  Member "joint cs mri reconstruction and segmentation with a unified deep network":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "brain segmentation from k space with end to end recurrent attention network")

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)