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 "novel evaluation of surgical activity recognition models using task based efficiency metrics"):
  Abstract: Purpose: Surgical task-based metrics (rather than entire procedure metrics) can be used to improve surgeon training and, ultimately, patient care through focused training interventions. Machine learning models to automatically recognize individual tasks or activities are needed to overcome the otherwise manual effort of video review. Traditionally, these models have been evaluated using frame-level accuracy. Here, we propose evaluating surgical activity recognition models by their effect on task-based efficiency metrics. In this way, we can determine when models have achieved adequate performance for providing surgeon feedback via metrics from individual tasks. #R##N#Methods: We propose a new CNN-LSTM model, RP-Net-V2, to recognize the 12 steps of robotic-assisted radical prostatectomies (RARP). We evaluated our model both in terms of conventional methods (e.g. Jaccard Index, task boundary accuracy) as well as novel ways, such as the accuracy of efficiency metrics computed from instrument movements and system events. #R##N#Results: Our proposed model achieves a Jaccard Index of 0.85 thereby outperforming previous models on robotic-assisted radical prostatectomies. Additionally, we show that metrics computed from tasks automatically identified using RP-Net-V2 correlate well with metrics from tasks labeled by clinical experts. #R##N#Conclusions: We demonstrate that metrics-based evaluation of surgical activity recognition models is a viable approach to determine when models can be used to quantify surgical efficiencies. We believe this approach and our results illustrate the potential for fully automated, post-operative efficiency reports.

Includes members:
  Member "future state predicting lstm for early surgery type recognition":
    also connected through other hyperedges:
      (none)
  Member "joint surgical gesture and task classification with multi task and multimodal learning":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "multi task recurrent neural network for surgical gesture recognition and progress prediction")
  Member "surgical activity recognition in robot assisted radical prostatectomy using deep learning":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "temporal segmentation of surgical sub tasks through deep learning with multiple data sources")
  Member "segmental spatio temporal cnns for fine grained action segmentation and classification":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "trespassing the boundaries labeling temporal bounds for object interactions in egocentric video")
  Member "recognizing surgical activities with recurrent neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "multi task recurrent neural network for surgical gesture recognition and progress prediction")
  Member "endonet a deep architecture for recognition tasks on laparoscopic videos":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "multi task recurrent neural network for surgical gesture recognition and progress prediction")
  Member "segmental spatiotemporal cnns for fine grained action segmentation":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "a novel online action detection framework from untrimmed video streams")

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)