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 "ap mtl attention pruned multi task learning model for real time instrument detection and segmentation in robot assisted surgery"):
  Abstract: Surgical scene understanding and multi-tasking learning are crucial for image-guided robotic surgery. Training a real-time robotic system for the detection and segmentation of high-resolution images provides a challenging problem with the limited computational resource. The perception drawn can be applied in effective real-time feedback, surgical skill assessment, and human-robot collaborative surgeries to enhance surgical outcomes. For this purpose, we develop a novel end-to-end trainable real-time Multi-Task Learning (MTL) model with weight-shared encoder and task-aware detection and segmentation decoders. Optimization of multiple tasks at the same convergence point is vital and presents a complex problem. Thus, we propose an asynchronous task-aware optimization (ATO) technique to calculate task-oriented gradients and train the decoders independently. Moreover, MTL models are always computationally expensive, which hinder real-time applications. To address this challenge, we introduce a global attention dynamic pruning (GADP) by removing less significant and sparse parameters. We further design a skip squeeze and excitation (SE) module, which suppresses weak features, excites significant features and performs dynamic spatial and channel-wise feature re-calibration. Validating on the robotic instrument segmentation dataset of MICCAI endoscopic vision challenge, our model significantly outperforms state-of-the-art segmentation and detection models, including best-performed models in the challenge.

Includes members:
  Member "real time joint object detection and semantic segmentation network for automated driving":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "dynamic task weighting methods for multi task networks in autonomous driving systems")
  Member "learning where to look while tracking instruments in robot assisted surgery":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "lc gan image to image translation based on generative adversarial network for endoscopic images")
  Member "2017 robotic instrument segmentation challenge":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "lc gan image to image translation based on generative adversarial network for endoscopic images")
  Member "visualizing uncertainty and saliency maps of deep convolutional neural networks for medical imaging applications":
    also connected through other hyperedges:
      (none)
  Member "bisenet bilateral segmentation network for real time semantic segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "highly efficient salient object detection with 100k parameters")
  Member "yolov3 an incremental improvement":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "perception and decision making of autonomous systems in the era of learning an overview")
  Member "automatic instrument segmentation in robot assisted surgery using deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "lc gan image to image translation based on generative adversarial network for endoscopic images")
  Member "ternausnet u net with vgg11 encoder pre trained on imagenet for image segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "lc gan image to image translation based on generative adversarial network for endoscopic images")

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)