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: "video and accelerometer based motion analysis for automated surgical skills assessment"
  Abstract: Purpose: Basic surgical skills of suturing and knot tying are an essential part of medical training. Having an automated system for surgical skills assessment could help save experts time and improve training efficiency. There have been some recent attempts at automated surgical skills assessment using either video analysis or acceleration data. In this paper, we present a novel approach for automated assessment of OSATS based surgical skills and provide an analysis of different features on multi-modal data (video and accelerometer data). Methods: We conduct the largest study, to the best of our knowledge, for basic surgical skills assessment on a dataset that contained video and accelerometer data for suturing and knot-tying tasks. We introduce "entropy based" features - Approximate Entropy (ApEn) and Cross-Approximate Entropy (XApEn), which quantify the amount of predictability and regularity of fluctuations in time-series data. The proposed features are compared to existing methods of Sequential Motion Texture (SMT), Discrete Cosine Transform (DCT) and Discrete Fourier Transform (DFT), for surgical skills assessment. Results: We report average performance of different features across all applicable OSATS criteria for suturing and knot tying tasks. Our analysis shows that the proposed entropy based features out-perform previous state-of-the-art methods using video data. For accelerometer data, our method performs better for suturing only. We also show that fusion of video and acceleration features can improve overall performance with the proposed entropy features achieving highest accuracy. Conclusions: Automated surgical skills assessment can be achieved with high accuracy using the proposed entropy features. Such a system can significantly improve the efficiency of surgical training in medical schools and teaching hospitals.

Connected through hyperedge HE-1 (induced by source paper "manipulation skill assessment from videos with spatial attention network"):
  with members:
    - "grad cam visual explanations from deep networks via gradient based localization"
    - "mutual context network for jointly estimating egocentric gaze and actions"
    - "boosted attention leveraging human attention for image captioning"
    - "bam bottleneck attention module"
    - "learning to score the figure skating sports videos"
    - "predicting gaze in egocentric video by learning task dependent attention transition"
    - "tool detection and operative skill assessment in surgical videos using region based convolutional neural networks"
    - "cbam convolutional block attention module"

Connected through hyperedge HE-2 (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"
    - "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-3 (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"
    - "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-4 (induced by source paper "the pros and cons rank aware temporal attention for skill determination in long videos"):
  with members:
    - "scaling egocentric vision the epic kitchens dataset"
    - "learning to score the figure skating sports videos"
    - "w talc weakly supervised temporal activity localization and classification"
    - "deep learning with convolutional neural network for objective skill evaluation in robot assisted surgery"
    - "where and when to look deep siamese attention networks for video based person re identification"
    - "diversity regularized spatiotemporal attention for video based person re identification"
    - "outrageously large neural networks the sparsely gated mixture of experts layer"
    - "quo vadis action recognition a new model and the kinetics dataset"

Connected through hyperedge HE-5 (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"
    - "evaluationnet can human skill be evaluated by deep networks"
    - "automated surgical skill assessment in rmis training"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "automated surgical skill assessment in rmis training"):
  with members:
    - "recognizing surgical activities with recurrent neural networks"
    - "machine learning approach for skill evaluation in robotic assisted surgery"
    - "augmenting bag of words data driven discovery of temporal and structural information for activity recognition"

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)