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: "measuring the quality of exercises"
  Abstract: This work explores the problem of exercise quality measurement since it is essential for effective management of diseases like cerebral palsy (CP). This work examines the assessment of quality of large amplitude movement (LAM) exercises designed to treat CP in an automated fashion. Exercise data was collected by trained participants to generate ideal examples to use as a positive samples for machine learning. Following that, subjects were asked to deliberately make subtle errors during the exercise, such as restricting movements, as is commonly seen in cases of patients suffering from CP. The quality measurement problem was then posed as a classification to determine whether an example exercise was either "good" or "bad". Popular machine learning techniques for classification, including support vector machines (SVM), single and doublelayered neural networks (NN), boosted decision trees, and dynamic time warping (DTW), were compared. The AdaBoosted tree performed best with an accuracy of 94.68% demonstrating the feasibility of assessing exercise quality.

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"
    - "evaluating surgical skills from kinematic data using convolutional neural networks"
    - "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"

Connected through hyperedge HE-2 (induced by source paper "action quality assessment across multiple actions"):
  with members:
    - "taskonomy disentangling task transfer learning"
    - "who s better who s best pairwise deep ranking for skill determination"
    - "am i a baller basketball performance assessment from first person videos"
    - "learning to score olympic events"
    - "adam a method for stochastic optimization"
    - "how transferable are features in deep neural networks"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "caffe convolutional architecture for fast feature embedding"

Connected through hyperedge HE-3 (induced by source paper "learning to score olympic events"):
  with members:
    - "long term temporal convolutions for action recognition"
    - "beyond short snippets deep networks for video classification"
    - "learning spatiotemporal features with 3d convolutional networks"
    - "caffe convolutional architecture for fast feature embedding"
    - "ucf101 a dataset of 101 human actions classes from videos in the wild"

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)