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 "chessboard and chess piece recognition with the support of neural networks"):
  Abstract: Chessboard and chess piece recognition is a computer vision problem that has not yet been efficiently solved. However, its solution is crucial for many experienced players who wish to compete against AI bots, but also prefer to make decisions based on the analysis of a physical chessboard. It is also important for organizers of chess tournaments who wish to digitize play for online broadcasting or ordinary players who wish to share their gameplay with friends. Typically, such digitization tasks are performed by humans or with the aid of specialized chessboards and pieces. However, neither solution is easy or convenient. To solve this problem, we propose a novel algorithm for digitizing chessboard configurations. #R##N#We designed a method that is resistant to lighting conditions and the angle at which images are captured, and works correctly with numerous chessboard styles. The proposed algorithm processes pictures iteratively. During each iteration, it executes three major sub-processes: detecting straight lines, finding lattice points, and positioning the chessboard. Finally, we identify all chess pieces and generate a description of the board utilizing standard notation. For each of these steps, we designed our own algorithm that surpasses existing solutions. We support our algorithms by utilizing machine learning techniques whenever possible. #R##N#The described method performs extraordinarily well and achieves an accuracy over $99.5\%$ for detecting chessboard lattice points (compared to the $74\%$ for the best alternative), $95\%$ (compared to $60\%$ for the best alternative) for positioning the chessboard in an image, and almost $95\%$ for chess piece recognition.

Includes members:
  Member "alive caricature from 2d to 3d":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "3d carigan an end to end solution to 3d caricature generation from face photos")
  Member "weakly supervised localization using deep feature maps":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "dual attention guided dropblock module for weakly supervised object localization")
  Member "large scale analysis of chess games with chess engines a preliminary report":
    also connected through other hyperedges:
      (none)
  Member "hybridization of otsu method and median filter for color image segmentation":
    also connected through other hyperedges:
      (none)
  Member "chess quick and robust detection of chess board features":
    also connected through other hyperedges:
      (none)

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)