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 sequential segmentation of organs in volumetric medical scans"):
  Abstract: Segmentation in 3-D scans is playing an increasingly important role in current clinical practice supporting diagnosis, tissue quantification, or treatment planning. The current 3-D approaches based on convolutional neural networks usually suffer from at least three main issues caused predominantly by implementation constraints–first, they require resizing the volume to the lower-resolutional reference dimensions, and second, the capacity of such approaches is very limited due to memory restrictions, and third, all slices of volumes have to be available at any given training or testing time. We address these problems by a U-Net-like architecture consisting of bidirectional convolutional long short-term memory and convolutional, pooling, upsampling, and concatenation layers enclosed into time-distributed wrappers. Our network can either process the full volumes in a sequential manner or segment slabs of slices on demand. We demonstrate performance of our architecture on vertebrae and liver segmentation tasks in 3-D computed tomography scans.

Includes members:
  Member "densely dilated spatial pooling convolutional network using benign loss functions for imbalanced volumetric prostate segmentation":
    also connected through other hyperedges:
      (none)
  Member "lstm a search space odyssey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "refined gate a simple and effective gating mechanism for recurrent units")
  Member "automatic 3d cardiovascular mr segmentation with densely connected volumetric convnets":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "boundary weighted domain adaptive neural network for prostate mr image segmentation")
  Member "deep supervision for pancreatic cyst segmentation in abdominal ct scans":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "phase collaborative network for two phase medical image segmentation")
  Member "an exploration of 2d and 3d deep learning techniques for cardiac mr image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "deep learning in cardiology")
  Member "h denseunet hybrid densely connected unet for liver and liver tumor segmentation from ct volumes":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "convolutional networks with dense connectivity")
  Member "extracting 3d vascular structures from microscopy images using convolutional recurrent networks":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "forecast clstm a new convolutional lstm network for cloudage nowcasting")
  Member "automatic brain tumor detection and segmentation using u net based fully convolutional networks":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "res3atn deep 3d residual attention network for hand gesture recognition in videos")

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)