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: "machine assisted authentication of paper currency an experiment on indian banknotes"
  Abstract: Automatic authentication of paper money has been targeted. Indian bank notes are taken as reference to show how a system can be developed for discriminating fake notes from genuine ones. Image processing and pattern recognition techniques are used to design the overall approach. The ability of the embedded security aspects is thoroughly analysed for detecting fake currencies. Real forensic samples are involved in the experiment that shows a high precision machine can be developed for authentication of paper money. The system performance is reported in terms of both accuracy and processing speed. Comparison with human subjects namely forensic experts and bank staffs clearly shows its applicability for mass checking of currency notes in the real world. The analysis of security features to protect counterfeiting highlights some facts that should be taken care of in future designing of currency notes.

Connected through hyperedge HE-1 (induced by source paper "identity document and banknote security forensics a survey"):
  with members:
    - "midv 500 a dataset for identity document analysis and recognition on mobile devices in video stream"
    - "learning rich features for image manipulation detection"
    - "fighting fake news image splice detection via learned self consistency"
    - "noiseprint a cnn based camera model fingerprint"
    - "camera based image forgery localization using convolutional neural networks"
    - "e counterfeit a mobile server platform for document counterfeit detection"
    - "humans are easily fooled by digital images"

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)