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 "deep learning for image segmentation a short survey"):
  Abstract: Deep learning works as a discrete non-linear mapping function and has achieved great success as a powerful classification tool. However, is deep learning omnipotent? This paper gives a short survey of the accuracy achieved by deep learning so far in image segmentation. Compared to the close to 100% classification accuracy achieved by deep learning, the image segmentation accuracy achieved by deep learning is only about 80%. We analyze the possible reasons why deep learning could not achieve acceptable accuracy in image segmentation and found that deep learning only generates a prediction map and relies on other segmentation methods to complete the segmentation task. In addition, the performance of deep learning is determined by the number of outputs. Consequently, deep learning could not achieve high segmentation accuracy unless the resolution of the image is extremely small.

Includes members:
  Member "deep learning a critical appraisal":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "the next decade in ai four steps towards robust artificial intelligence")
  Member "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "relationship embedded representation learning for grounding referring expressions")
  Member "towards biologically plausible deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sideways depth parallel training of video models")
  Member "a combined deep learning and deformable model approach to fully automatic segmentation of the left ventricle in cardiac mri":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning in cardiology")
  Member "automatic 3d liver segmentation using sparse representation of global and local image information via level set formulation":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "3d deeply supervised network for automatic liver segmentation from ct volumes")
  Member "multi column deep neural networks for image classification":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "importance driven deep learning system testing")

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)