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 "a cnn based approach to classify cricket bowlers based on their bowling actions"):
  Abstract: With the advances in hardware technologies and deep learning techniques, it has become feasible to apply these techniques in diverse fields. Convolutional Neural Network (CNN), an architecture from the field of deep learning, has revolutionized Computer Vision. Sports is one of the avenues in which the use of computer vision is thriving. Cricket is a complex game consisting of different types of shots, bowling actions and many other activities. Every bowler, in a game of cricket, bowls with a different bowling action. We leverage this point to identify different bowlers. In this paper, we have proposed a CNN model to identify eighteen different cricket bowlers based on their bowling actions using transfer learning. Additionally, we have created a completely new dataset containing 8100 images of these eighteen bowlers to train the proposed framework and evaluate its performance. We have used the VGG16 model pre-trained with the ImageNet dataset and added a few layers on top of it to build our model. After trying out different strategies, we found that freezing the weights for the first 14 layers of the network and training the rest of the layers works best. Our approach achieves an overall average accuracy of 93.3% on the test set and converges to a very low cross-entropy loss.

Includes members:
  Member "efficient processing of deep neural networks a tutorial and survey":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "searching for winograd aware quantized networks")
  Member "variants of rmsprop and adagrad with logarithmic regret bounds":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "compositional adam an adaptive compositional solver")
  Member "understanding convolutional neural networks with a mathematical model":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sbert wk a sentence embedding method by dissecting bert based word models")
  Member "how transferable are features in deep neural networks":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "secure and robust machine learning for healthcare a survey")
  Member "very deep convolutional networks for large scale image recognition":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "discrimination aware network pruning for deep model compression")

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.CV(Computer Vision and Pattern Recognition)