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 "nnu net breaking the spell on successful medical image segmentation"):
  Abstract: Fueled by the diversity of datasets, semantic segmentation is a popular subfield in medical image analysis with a vast number of new methods being proposed each year. This ever-growing jungle of methodologies, however, becomes increasingly impenetrable. At the same time, many proposed methods fail to generalize beyond the experiments they were demonstrated on, thus hampering the process of developing a segmentation algorithm on a new dataset. Here we present nnU-Net ('no-new-Net'), a framework that automatically adapts itself to any given new dataset. While this process was completely human-driven so far, we make a first attempt to automate necessary adaptations such as preprocessing, the exact patch size, batch size, and inference settings based on the properties of a given dataset. Remarkably, nnU-Net strips away the architectural bells and whistles that are typically proposed in the literature and relies on just a simple U-Net architecture embedded in a robust training scheme. Out of the box, nnU-Net achieves state of the art performance on six well-established segmentation challenges. Source code is available at this https URL.

Includes members:
  Member "a large annotated medical image dataset for the development and evaluation of segmentation algorithms":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the future of digital health with federated learning")
  Member "the liver tumor segmentation benchmark lits":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "multi organ segmentation over partially labeled datasets with multi scale feature abstraction")
  Member "why rankings of biomedical image analysis competitions should be interpreted with care":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "refuge challenge a unified framework for evaluating automated methods for glaucoma assessment from fundus photographs")
  Member "nnu net self adapting framework for u net based medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "3d medical image segmentation with labeled and unlabeled data using autoencoders at the example of liver segmentation in ct images")
  Member "autofocus layer for semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (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-6 (induced by source paper "super resolution using segmentation prior self attention generative adversarial network")
  Member "cnn based segmentation of medical imaging data":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "low memory neural network training a technical report")
  Member "v net fully convolutional neural networks for volumetric medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "secure and robust machine learning for healthcare a survey")

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)