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 "apricot variety classification using image processing and machine learning approaches"):
  Abstract: Apricot which is a cultivated type of Zerdali (wild apricot) has an important place in human nutrition and its medical properties are essential for human health. The objective of this research was to obtain a model for apricot mass and separate apricot variety with image processing technology using external features of apricot fruit. In this study, five verities of apricot were used. In order to determine the size of the fruits, three mutually perpendicular axes were defined, length, width, and thickness. Measurements show that the effect of variety on all properties was statistically significant at the 1% probability level. Furthermore, there is no significant difference between the estimated dimensions by image processing approach and the actual dimensions. The developed system consists of a digital camera, a light diffusion chamber, a distance adjustment pedestal, and a personal computer. Images taken by the digital camera were stored as (RGB) for further analysis. The images were taken for a number of 49 samples of each cultivar in three directions. A linear equation is recommended to calculate the apricot mass based on the length and the width with R 2 = 0.97. In addition, ANFIS model with C-means was the best model for classifying the apricot varieties based on the physical features including length, width, thickness, mass, and projected area of three perpendicular surfaces. The accuracy of the model was 87.7.

Includes members:
  Member "deep fruit detection in orchards":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "ellipse r cnn learning to infer elliptical object from clustering and occlusion")
  Member "imagenet large scale visual recognition challenge":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "using a thousand optimization tasks to learn hyperparameter search strategies")

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)