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 "fuzzy semantic segmentation of breast ultrasound image with breast anatomy constraints"):
  Abstract: Breast cancer is one of the most serious disease affects women's health. Due to low cost, portable, no radiation, and high efficiency, breast ultrasound (BUS) imaging is the most popular approach for diagnosing early breast cancer. However, ultrasound images are low resolution and poor quality, developing accurate detection system is a challenging task. In this paper, we propose a fully automatic segmentation algorithm consisting of two parts: fuzzy fully convolutional network and accurately fine-tuning post-processing based on breast anatomy constraints. In the first part, the image is preprocessed by contrast enhancement, and wavelet features are employed for image augmentation. A fuzzy membership function transforms the augmented BUS images into fuzzy domain. The features from convolutional layers are processed using fuzzy logic as well. The conditional random fields (CRFs) post-process the segmentation result. The location relation among the breast anatomy layers is utilized to improve the performance. The proposed method is applied to the dataset with 325 BUS images, and achieves state-of-art performance compared with that of existing methods with true positive rate 90.33%, false positive rate 9.00%, and intersection over union (IoU) 81.29% on tumor category, and overall intersection over union (mIoU) 80.47% over five categories: fat layer, mammary layer, muscle layer, background, and tumor.

Includes members:
  Member "an end to end deep learning histochemical scoring system for breast cancer tma":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "reversed active learning based atrous densenet for pathological image classification")
  Member "a benchmark for breast ultrasound image segmentation busis":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "a deep learning framework for single sided sound speed inversion in medical ultrasound")
  Member "adversarial deep structured nets for mass segmentation from mammograms":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "end to end face parsing via interlinked convolutional neural networks")
  Member "automatic breast ultrasound image segmentation a survey":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "breast anatomy enriched tumor saliency estimation")
  Member "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "semantic image segmentation via deep parsing network":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "attention guided chained context aggregation for semantic segmentation")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "conditional random fields as recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "real time high performance semantic image segmentation of urban street scenes")

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)