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 adversarial context aware landmark detection for ultrasound imaging"):
  Abstract: Real-time localization of prostate gland in trans-rectal ultrasound images is a key technology that is required to automate the ultrasound guided prostate biopsy procedures. In this paper, we propose a new deep learning based approach which is aimed at localizing several prostate landmarks efficiently and robustly. We propose a multitask learning approach primarily to make the overall algorithm more contextually aware. In this approach, we not only consider the explicit learning of landmark locations, but also build-in a mechanism to learn the contour of the prostate. This multitask learning is further coupled with an adversarial arm to promote the generation of feasible structures. We have trained this network using ~4000 labeled trans-rectal ultrasound images and tested on an independent set of images with ground truth landmark locations. We have achieved an overall Dice score of 92.6% for the adversarially trained multitask approach, which is significantly better than the Dice score of 88.3% obtained by only learning of landmark locations. The overall mean distance error using the adversarial multitask approach has also improved by 20% while reducing the standard deviation of the error compared to learning landmark locations only. In terms of computational complexity both approaches can process the images in real-time using standard computer with a standard CUDA enabled GPU.

Includes members:
  Member "stable distribution alignment using the dual of the adversarial distance":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "a review of domain adaptation without target labels")
  Member "adversarial discriminative domain adaptation":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "unsupervised multi class domain adaptation theory algorithms and practice")
  Member "adversarial posenet a structure aware convolutional network for human pose estimation":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lightweight 3d human pose estimation network training using teacher student learning")
  Member "fine grained recurrent neural networks for automatic prostate segmentation in ultrasound images":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "boundary weighted domain adaptive neural network for prostate mr image segmentation")
  Member "conditional random fields as recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "real time high performance semantic image segmentation of urban street scenes")
  Member "semantic image segmentation with deep convolutional nets and fully connected crfs":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "attention guided chained context aggregation for semantic segmentation")

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)