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 "tomaat volumetric medical image analysis as a cloud service"):
  Abstract: Deep learning has been recently applied to a multitude of computer vision and medical image analysis problems. Although recent research efforts have improved the state of the art, most of the methods cannot be easily accessed, compared or used by either researchers or the general public. Researchers often publish their code and trained models on the internet, but this does not always enable these approaches to be easily used or integrated in stand-alone applications and existing workflows. In this paper we propose a framework which allows easy deployment and access of deep learning methods for segmentation through a cloud-based architecture. Our approach comprises three parts: a server, which wraps trained deep learning models and their pre- and post-processing data pipelines and makes them available on the cloud; a client which interfaces with the server to obtain predictions on user data; a service registry that informs clients about available prediction endpoints that are available in the cloud. These three parts constitute the open-source TOMAAT framework.

Includes members:
  Member "automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self normalizing neural network":
    also connected through other hyperedges:
      (none)
  Member "automatic brain tumor segmentation using cascaded anisotropic convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "one pass multi task networks with cross task guided attention for brain tumor segmentation")
  Member "3d u net learning dense volumetric segmentation from sparse annotation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "convolutional occupancy networks")
  Member "v net fully convolutional neural networks for volumetric medical image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (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)