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: "a new digital watermarking algorithm using combination of least significant bit lsb and inverse bit"
  Abstract: Software watermarking is one of the most important methods for protecting copyrights#R##N#and authenticating ownership; which can be used to prevent software piracy. In order to#R##N#provide high quality watermarked image, the watermarked image should be#R##N#imperceptible. Least significant bit (LSB) is a technique that has been used for digital#R##N#watermarking. Many researchers have improved LSB to embed watermark in the image.#R##N#In this thesis, we introduce an improved digital watermarking algorithm using least#R##N#significant bit (LSB). This method is chosen due to its minimal effect on the image. LSB#R##N#is used by inversing the binary values of the watermark text and shifting the watermark#R##N#according to the odd or even number of pixel coordinates of image before embedding#R##N#the watermark. The proposed algorithm can be personalized according to the length of#R##N#the watermark text. If the length of the watermark text is more than ((M*N)/8)-2 (where#R##N#M*N is the amount of pixels in the image), the proposed algorithm will also embed the#R##N#extra of the watermark text in the second LSB. We compare our proposed algorithm#R##N#with the 1-LSB algorithm and Lee’s algorithm and Yang's algorithm using Peak signalto-#R##N#noise ratio (PSNR). The value of the watermarked dock image is 54.5691 dB which is#R##N#higher than the traditional LSB 53.6950 dB and Lee's algorithm 53.7041 db and Yang's#R##N#algorithm 52.4518 dB. Also in the other image, the proposed algorithm got the highest#R##N#value of PSNR. This improved algorithm improved its quality of the watermarked#R##N#image. We also attack the watermarked image by using cropping and adding noise and#R##N#we got good results as well.

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.MM(Multimedia)