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 "accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks"):
  Abstract: Manual feedback from senior surgeons observing less experienced trainees is a laborious task that is very expensive, time-consuming and prone to subjectivity. With the number of surgical procedures increasing annually, there is an unprecedented need to provide an accurate, objective and automatic evaluation of trainees’ surgical skills in order to improve surgical practice. In this paper, we designed a convolutional neural network (CNN) to classify surgical skills by extracting latent patterns in the trainees’ motions performed during robotic surgery. The method is validated on the JIGSAWS dataset for two surgical skills evaluation tasks: classification and regression.#N# Our results show that deep neural networks constitute robust machine learning models that are able to reach new competitive state-of-the-art performance on the JIGSAWS dataset. While we leveraged from CNNs’ efficiency, we were able to minimize its black-box effect using the class activation map technique. This characteristic allowed our method to automatically pinpoint which parts of the surgery influenced the skill evaluation the most, thus allowing us to explain a surgical skill classification and provide surgeons with a novel personalized feedback technique. We believe this type of interpretable machine learning model could integrate within “Operation Room 2.0” and support novice surgeons in improving their skills to eventually become experts.

Includes members:
  Member "deep learning for time series classification a review":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "identity recognition in intelligent cars with behavioral data and lstm resnet classifier")
  Member "transfer learning for time series classification":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "meta learning framework with applications to zero shot time series forecasting")
  Member "deep learning with convolutional neural network for objective skill evaluation in robot assisted surgery":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "video based surgical skill assessment using 3d convolutional neural networks")
  Member "evaluating surgical skills from kinematic data using convolutional neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "action quality assessment using siamese network based deep metric learning")
  Member "surgical data science for next generation interventions":
    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 "automated surgical skill assessment in rmis training":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "what and how well you performed a multitask learning approach to action quality assessment")
  Member "time series classification from scratch with deep neural networks a strong baseline":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "socialgrid a tcn enhanced method for online discussion forecasting")
  Member "learning deep features for discriminative localization":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "ha ccn hierarchical attention based crowd counting network")

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.LG(Machine Learning)