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 "deep q learning driven ct pancreas segmentation with geometry aware u net"):
  Abstract: The segmentation of pancreas is important for medical image analysis, yet it faces great challenges of class imbalance, background distractions, and non-rigid geometrical features. To address these difficulties, we introduce a deep Q network (DQN) driven approach with deformable U-Net to accurately segment the pancreas by explicitly interacting with contextual information and extract anisotropic features from pancreas. The DQN-based model learns a context-adaptive localization policy to produce a visually tightened and precise localization bounding box of the pancreas. Furthermore, deformable U-Net captures geometry-aware information of pancreas by learning geometrically deformable filters for feature extraction. The experiments on NIH dataset validate the effectiveness of the proposed framework in pancreas segmentation.

Includes members:
  Member "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "elixirnet relation aware network architecture adaptation for medical lesion detection")
  Member "detection aided liver lesion segmentation using deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "feature fusion encoder decoder network for automatic liver lesion segmentation")
  Member "a 3d coarse to fine framework for volumetric medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "object contextual representations for semantic segmentation")
  Member "deformable convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "object detection in optical remote sensing images a survey and a new benchmark")
  Member "improving automated multiple sclerosis lesion segmentation with a cascaded 3d convolutional neural network approach":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "unpaired multi modal segmentation via knowledge distillation")
  Member "spatial aggregation of holistically nested convolutional neural networks for automated pancreas localization and segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "mcmc guided cnn training and segmentation for pancreas extraction")
  Member "tree structured reinforcement learning for sequential object localization":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "pixelrl fully convolutional network with reinforcement learning for image processing")
  Member "spatial aggregation of holistically nested networks for automated pancreas segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "mcmc guided cnn training and segmentation for pancreas extraction")

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)