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 "a topological loss function for deep learning based image segmentation using persistent homology"):
  Abstract: We introduce a method for training neural networks to perform image or volume segmentation in which prior knowledge about the topology of the segmented object can be explicitly provided and then incorporated into the training process. By using the differentiable properties of persistent homology, a concept used in topological data analysis, we can specify the desired topology of segmented objects in terms of their Betti numbers and then drive the proposed segmentations to contain the specified topological features. Importantly this process does not require any ground-truth labels, just prior knowledge of the topology of the structure being segmented. We demonstrate our approach in three experiments. Firstly we create a synthetic task in which handwritten MNIST digits are de-noised, and show that using this kind of topological prior knowledge in the training of the network significantly improves the quality of the de-noised digits. Secondly we perform an experiment in which the task is segmenting the myocardium of the left ventricle from cardiac magnetic resonance images. We show that the incorporation of the prior knowledge of the topology of this anatomy improves the resulting segmentations in terms of both the topological accuracy and the Dice coefficient. Thirdly, we extend the method to 3D volumes and demonstrate its performance on the task of segmenting the placenta from ultrasound data, again showing that incorporating topological priors improves performance on this challenging task. We find that embedding explicit prior knowledge in neural network segmentation tasks is most beneficial when the segmentation task is especially challenging and that it can be used in either a semi-supervised or post-processing context to extract a useful training gradient from images without pixelwise labels.

Includes members:
  Member "phom gem persistent homology for generative models":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "from persistent homology to reinforcement learning with applications for retail banking")
  Member "explicit topological priors for deep learning based image segmentation using persistent homology":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "model based and data driven strategies in medical image computing")
  Member "a topology layer for machine learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "efficient topological layer based on persistent landscapes")
  Member "neural persistence a complexity measure for deep neural networks using algebraic topology":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "topologically densified distributions")
  Member "connectivity optimized representation learning via persistent homology":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "efficient topological layer based on persistent landscapes")
  Member "toporeg a topological regularizer for classifiers":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "explicit topological priors for deep learning based image segmentation using persistent homology")
  Member "a probabilistic u net for segmentation of ambiguous images":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "probabilistic future prediction for video scene understanding")
  Member "generalised dice overlap as a deep learning loss function for highly unbalanced segmentations":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "on the road with 16 neurons mental imagery with bio inspired deep neural networks")

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)