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 "semantic guided encoder feature learning for blurry boundary delineation"):
  Abstract: Encoder-decoder architectures are widely adopted for medical image segmentation tasks. With the lateral skip connection, the models can obtain and fuse both semantic and resolution information in deep layers to achieve more accurate segmentation performance. However, in many applications (e.g., blurry boundary images), these models often cannot precisely locate complex boundaries and segment tiny isolated parts. To solve this challenging problem, we firstly analyze why simple skip connections are not enough to help accurately locate indistinct boundaries and argue that it is due to the fuzzy information in the skip connection provided in the encoder layers. Then we propose a semantic-guided encoder feature learning strategy to learn both high resolution and rich semantic encoder features so that we can more accurately locate the blurry boundaries, which can also enhance the network by selectively learning discriminative features. Besides, we further propose a soft contour constraint mechanism to model the blurry boundary detection. Experimental results on real clinical datasets show that our proposed method can achieve state-of-the-art segmentation accuracy, especially for the blurry regions. Further analysis also indicates that our proposed network components indeed contribute to the improvement of performance. Experiments on additional datasets validate the generalization ability of our proposed method.

Includes members:
  Member "boundary weighted domain adaptive neural network for prostate mr image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi organ segmentation over partially labeled datasets with multi scale feature abstraction")
  Member "attention u net learning where to look for the pancreas":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "super resolution using segmentation prior self attention generative adversarial network")
  Member "imperfect segmentation labels how much do they matter":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "improving training on noisy stuctured labels")
  Member "concurrent spatial and channel squeeze excitation in fully convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "ap mtl attention pruned multi task learning model for real time instrument detection and segmentation in robot assisted surgery")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "fully convolutional networks for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "gan compression efficient architectures for interactive conditional gans")

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)