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: "vlsi friendly framework for scalable video coding based on compressed sensing"
  Abstract: This paper presents a new VLSI friendly framework for scalable video coding based on Compressed Sensing (CS). It achieves scalability through 3-Dimensional Discrete Wavelet Transform (3-D DWT) and better compression ratio by exploiting the inherent sparsity of the high-frequency wavelet sub-bands through CS. By using 3-D DWT and a proposed adaptive measurement scheme called AMS at the encoder, one can succeed in improving the compression ratio and reducing the complexity of the decoder. The proposed video codec uses only 7% of the total number of multipliers needed in a conventional CS-based video coding system. A codebook of Bernoulli matrices with different sizes corresponding to the predefined sparsity levels is maintained at both the encoder and the decoder. Based on the calculated l0-norm of the input vector, one of the sixteen possible Bernoulli matrices will be selected for taking the CS measurements and its index will be transmitted along with the measurements. Based on this index, the corresponding Bernoulli matrix has been used in CS reconstruction algorithm to get back the high-frequency wavelet sub-bands at the decoder. At the decoder, a new Enhanced Approximate Message Passing (EAMP) algorithm has been proposed to reconstruct the wavelet coefficients and apply the inverse wavelet transform for restoring back the video frames. Simulation results have established the superiority of the proposed framework over the existing schemes and have increased its suitability for VLSI implementation. Moreover, the coded video is found to be scalable with an increase in a number of levels of wavelet decomposition.

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)