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 "active learning for segmentation based on bayesian sample queries"):
  Abstract: Segmentation of anatomical structures is a fundamental image analysis task for many applications in the medical field. Deep learning methods have been shown to perform well, but for this purpose large numbers of manual annotations are needed in the first place, which necessitate prohibitive levels of resources that are often unavailable. In an active learning framework of selecting informed samples for manual labeling, expert clinician time for manual annotation can be optimally utilized, enabling the establishment of large labeled datasets for machine learning. In this paper, we propose a novel method that combines representativeness with uncertainty in order to estimate ideal samples to be annotated, iteratively from a given dataset. Our novel representativeness metric is based on Bayesian sampling, by using information-maximizing autoencoders. We conduct experiments on a shoulder magnetic resonance imaging (MRI) dataset for the segmentation of four musculoskeletal tissue classes. Quantitative results show that the annotation of representative samples selected by our proposed querying method yields an improved segmentation performance at each active learning iteration, compared to a baseline method that also employs uncertainty and representativeness metrics. For instance, with only 10% of the dataset annotated, our method reaches within 5% of Dice score expected from the upper bound scenario of all the dataset given as annotated (an impractical scenario due to resource constraints), and this gap drops down to a mere 2% when less than a fifth of the dataset samples are annotated. Such active learning approach to selecting samples to annotate enables an optimal use of the expert clinician time, being often the bottleneck in realizing machine learning solutions in medicine.

Includes members:
  Member "bts dsn deeply supervised neural network with short connections for retinal vessel segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "uncertainty and interpretability in convolutional neural networks for semantic segmentation of colorectal polyps")
  Member "variational adversarial active learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "task aware variational adversarial active learning")
  Member "geometry in active learning for binary and multi class image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "a survey on active learning and human in the loop deep learning for medical image analysis")
  Member "active learning for segmentation by optimizing content information for maximal entropy":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "a survey on deep learning of small sample in biomedical image analysis")
  Member "learn the new keep the old extending pretrained models with new anatomy and images":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "modeling the background for incremental learning in semantic segmentation")
  Member "deep bayesian active semi supervised learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "inhibited softmax for uncertainty estimation in neural networks")
  Member "suggestive annotation a deep active learning framework for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "reinforced active learning for image segmentation")
  Member "deep bayesian active learning with image data":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "action for better prediction")

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)