human: Given a node-centered hypergraph: <hypergraph>, where nodes represent papers and hyperedges represent sets of papers co-cited by a source paper.

Details of the hypergraph:
Center node: "evaluating surgical skills from kinematic data using convolutional neural networks"
  Abstract: The need for automatic surgical skills assessment is increasing, especially because manual feedback from senior surgeons observing junior surgeons is prone to subjectivity and time consuming. Thus, automating surgical skills evaluation is a very important step towards improving surgical practice. In this paper, we designed a Convolutional Neural Network (CNN) to evaluate surgeon skills by extracting patterns in the surgeon motions performed in robotic surgery. The proposed method is validated on the JIGSAWS dataset and achieved very competitive results with 100% accuracy on the suturing and needle passing tasks. While we leveraged from the CNNs efficiency, we also managed to mitigate its black-box effect using class activation map. This feature allows our method to automatically highlight which parts of the surgical task influenced the skill prediction and can be used to explain the classification and to provide personalized feedback to the trainee.

Connected through hyperedge HE-1 (induced by source paper "action quality assessment using siamese network based deep metric learning"):
  with members:
    - "what and how well you performed a multitask learning approach to action quality assessment"
    - "3d human action recognition with siamese lstm based deep metric learning"
    - "the pros and cons rank aware temporal attention for skill determination in long videos"
    - "learning spatio temporal representation with pseudo 3d residual networks"
    - "who s better who s best pairwise deep ranking for skill determination"
    - "stacked hourglass networks for human pose estimation"
    - "learning to score olympic events"
    - "measuring the quality of exercises"

Connected through hyperedge HE-2 (induced by source paper "deep learning for time series classification a review"):
  with members:
    - "detecting and interpreting myocardial infarction using fully convolutional neural networks"
    - "proximity forest an effective and scalable distance based classifier for time series"
    - "deep learning for sensor based activity recognition a survey"
    - "reservoir computing approaches for representation and classification of multivariate time series"
    - "cost sensitive convolution based neural networks for imbalanced time series classification"
    - "genesis of basic and multi layer echo state network recurrent autoencoders for efficient data representations"
    - "deep k nearest neighbors towards confident interpretable and robust deep learning"
    - "transfer learning for time series classification"

Connected through hyperedge HE-3 (induced by source paper "what and how well you performed a multitask learning approach to action quality assessment"):
  with members:
    - "manipulation skill assessment from videos with spatial attention network"
    - "learning to score the figure skating sports videos"
    - "the pros and cons rank aware temporal attention for skill determination in long videos"
    - "video and accelerometer based motion analysis for automated surgical skills assessment"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "automated surgical skill assessment in rmis training"
    - "who s better who s best pairwise deep ranking for skill determination"
    - "extraction and classification of diving clips from continuous video footage"

Connected through hyperedge HE-4 (induced by source paper "video based surgical skill assessment using 3d convolutional neural networks"):
  with members:
    - "deep learning with convolutional neural network for objective skill evaluation in robot assisted surgery"
    - "tool detection and operative skill assessment in surgical videos using region based convolutional neural networks"
    - "quo vadis action recognition a new model and the kinetics dataset"
    - "video and accelerometer based motion analysis for automated surgical skills assessment"
    - "temporal segment networks for action recognition in videos"
    - "automated surgical skill assessment in rmis training"
    - "who s better who s best pairwise deep ranking for skill determination"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-5 (induced by source paper "deep neural network ensembles for time series classification"):
  with members:
    - "adversarial attacks on deep neural networks for time series classification"
    - "deep learning for time series classification a review"
    - "proximity forest an effective and scalable distance based classifier for time series"
    - "deep neural network ensemble by data augmentation and bagging for skin lesion classification"
    - "transfer learning for time series classification"
    - "data augmentation using synthetic data for time series classification with deep residual networks"
    - "towards a universal neural network encoder for time series"
    - "time series classification from scratch with deep neural networks a strong baseline"

Connected through hyperedge HE-6 (induced by source paper "adversarial attacks on deep neural networks for time series classification"):
  with members:
    - "deep neural network ensembles for time series classification"
    - "deep learning for time series classification a review"
    - "transfer learning for time series classification"
    - "data augmentation using synthetic data for time series classification with deep residual networks"
    - "adversarial examples for evaluating reading comprehension systems"
    - "technical report on the cleverhans v2 1 0 adversarial examples library"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "adversarial examples in the physical world"

Connected through hyperedge HE-7 (induced by source paper "accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks"):
  with members:
    - "deep learning for time series classification a review"
    - "transfer learning for time series classification"
    - "deep learning with convolutional neural network for objective skill evaluation in robot assisted surgery"
    - "surgical data science for next generation interventions"
    - "automated surgical skill assessment in rmis training"
    - "time series classification from scratch with deep neural networks a strong baseline"
    - "learning deep features for discriminative localization"
    - "adam a method for stochastic optimization"

Connected through hyperedge HE-8 (induced by source paper "automatic alignment of surgical videos using kinematic data"):
  with members:
    - "manipulation skill assessment from videos with spatial attention network"

Question: Please tell me which class the center node 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)