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 "multi level activation for segmentation of hierarchically nested classes"):
  Abstract: For a number of biomedical image segmentation tasks, including topological knowledge on the inter-class relationships, such as containment/nesting, can greatly improve the results. However, most `out-of-the-box' CNN models are still blind to this type of prior information. In this paper, we address the case of nested classes, and propose a novel approach to encode this information in the network, stepping away from the paradigmatic soft-max activation. Instead, we introduce a multi-level activation layer, as a generalization of logistic regression, and propose three compatible losses. We benchmark all of them on nuclei segmentation in bright-field microscopy cell images from the 2018 Data Science Bowl challenge, offering an exemplary segmentation task with cells and nested intra-cellular structures. Our scheme significantly outperforms standard multi-class classification with soft-max activation and a previously proposed method stemming from it, improving the Dice score significantly (p-values<0.002). On top of being conceptually simple, our approach is easy to implement and can be integrated in any CNN architecture. It can be generalized to a higher number of classes, with or without further relations of containment.

Includes members:
  Member "generalised wasserstein dice score for imbalanced multi class segmentation using holistic convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "deep convolutional neural networks for brain image analysis on magnetic resonance imaging a review")
  Member "deep learning markov random field for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "super resolution using segmentation prior self attention generative adversarial network")
  Member "automatic liver and lesion segmentation in ct using cascaded fully convolutional neural networks and 3d conditional random fields":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "multiresunet rethinking the u net architecture for multimodal biomedical image segmentation")
  Member "u net convolutional networks for biomedical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "adam a method for stochastic optimization":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)