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: "automated surgical skill assessment in rmis training"
  Abstract: Purpose: Manual feedback in basic RMIS training can consume a significant amount of time from expert surgeons' schedule and is prone to subjectivity. While VR-based training tasks can generate automated score reports, there is no mechanism of generating automated feedback for surgeons performing basic surgical tasks in RMIS training. In this paper, we explore the usage of different holistic features for automated skill assessment using only robot kinematic data and propose a weighted feature fusion technique for improving score prediction performance. #R##N#Methods: We perform our experiments on the publicly available JIGSAWS dataset and evaluate four different types of holistic features from robot kinematic data - Sequential Motion Texture (SMT), Discrete Fourier Transform (DFT), Discrete Cosine Transform (DCT) and Approximate Entropy (ApEn). The features are then used for skill classification and exact skill score prediction. Along with using these features individually, we also evaluate the performance using our proposed weighted combination technique. #R##N#Results: Our results demonstrate that these holistic features outperform all previous HMM based state-of-the-art methods for skill classification on the JIGSAWS dataset. Also, our proposed feature fusion strategy significantly improves performance for skill score predictions achieving up to 0.61 average spearman correlation coefficient. #R##N#Conclusions: Holistic features capturing global information from robot kinematic data can successfully be used for evaluating surgeon skill in basic surgical tasks on the da Vinci robot. Using the framework presented can potentially allow for real time score feedback in RMIS training.

Connected through hyperedge HE-1 (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"
    - "evaluating surgical skills from kinematic data using convolutional neural networks"
    - "video and accelerometer based motion analysis for automated surgical skills assessment"
    - "can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet"
    - "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-2 (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"
    - "evaluating surgical skills from kinematic data using convolutional neural networks"
    - "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"
    - "who s better who s best pairwise deep ranking for skill determination"
    - "the kinetics human action video dataset"

Connected through hyperedge HE-3 (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"
    - "evaluating surgical skills from kinematic data using convolutional neural networks"
    - "surgical data science for next generation interventions"
    - "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-4 (induced by source paper "deep learning with convolutional neural network for objective skill evaluation in robot assisted surgery"):
  with members:
    - "revisiting unreasonable effectiveness of data in deep learning era"
    - "instrument orientation based metrics for surgical skill evaluation in robot assisted and open needle driving"
    - "deep learning for time series analysis"
    - "data augmentation of wearable sensor data for parkinson s disease monitoring using convolutional neural networks"
    - "recognizing surgical activities with recurrent neural networks"
    - "multi scale convolutional neural networks for time series classification"
    - "deep learning in neural networks"
    - "max pooling dropout for regularization of convolutional neural networks"

Connected through hyperedge HE-5 (induced by source paper "evaluating surgical skills from kinematic data using convolutional neural networks"):
  with members:
    - "surgical data science for next generation interventions"
    - "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-6 (induced by source paper "who s better who s best pairwise deep ranking for skill determination"):
  with members:
    - "joint discovery of object states and manipulation actions"
    - "video and accelerometer based motion analysis for automated surgical skills assessment"
    - "evaluationnet can human skill be evaluated by deep networks"
    - "am i a baller basketball performance assessment from first person videos"
    - "learning to score olympic events"
    - "temporal segment networks towards good practices for deep action recognition"
    - "top down neural attention by excitation backprop"
    - "unsupervised learning from narrated instruction videos"

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)