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 "probability map guided bi directional recurrent unet for pancreas segmentation"):
  Abstract: Pancreatic cancer is one of the most lethal cancers as morbidity approximates mortality. A method for accurately segmenting the pancreas can assist doctors in the diagnosis and treatment of pancreatic cancer, but huge differences in shape and volume bring difficulties in segmentation. Among the current widely used approaches, the 2D method ignores the spatial information, and the 3D model is limited by high resource consumption and GPU memory occupancy. To address these issues, we propose a bi-directional recurrent UNet based on probabilistic map guidance (PBR-UNet). PBR-UNet includes a feature extraction module for extracting pixel-level probabilistic maps and a bi-directional recurrent module for fine segmentation. The extracted probabilistic map will be used to guide the fine segmentation and bi-directional recurrent module integrates contextual information into the entire network to avoid the loss of spatial information in propagation. By combining the probabilistic map of the adjacent slices with the bi-directional recurrent segmentation of intermediary slice, this paper solves the problem that the 2D network loses three-dimensional information and the 3D model leads to large computational resource consumption. We used Dice similarity coefficients (DSC) to evaluate our approach on NIH pancreatic datasets and eventually achieved a competitive result of 83.35%.

Includes members:
  Member "recurrently exploring class wise attention in a hybrid convolutional and bidirectional lstm network for multi label aerial image classification":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "building footprint generation by integratingconvolution neural network with feature pairwiseconditional random field fpcrf")
  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 "mds net a model driven stack based fully convolutional network for pancreas segmentation")
  Member "ra unet a hybrid deep attention aware network to extract liver and tumor in ct scans":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "brain tumor segmentation on mri with missing modalities")
  Member "an application of cascaded 3d fully convolutional networks 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 "3d semi supervised learning with uncertainty aware multi view co training":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "c2fnas coarse to fine neural architecture search for 3d medical image segmentation")
  Member "attention u net learning where to look for the pancreas":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "super resolution using segmentation prior self attention generative adversarial network")
  Member "automatic liver and tumor segmentation of ct and mri volumes using cascaded fully convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "gastric histopathology image segmentation using a hierarchical conditional random field")
  Member "recurrent saliency transformation network incorporating multi stage visual cues for small organ segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "elixirnet relation aware network architecture adaptation for medical lesion detection")

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)