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: "rate model for compressed video considering impacts of spatial temporal and amplitude resolutions and its applications for video coding and adaptation"
  Abstract: In this paper, we investigate the impacts of spatial, temporal and amplitude resolution (STAR) on the bit rate of a compressed video. We propose an analytical rate model in terms of the quantization stepsize, frame size and frame rate. Experimental results reveal that the increase of the video rate as the individual resolution increases follows a power function. Hence, the proposed model expresses the rate as the product of power functions of the quantization stepsize, frame size and frame rate, respectively. The proposed rate model is analytically tractable, requiring only four content dependent parameters. We also propose methods for predicting the model parameters from content features that can be computed from original video. Simulation results show that model predicted rates fit the measured data very well with high Pearson correlation (PC) and small relative root mean square error (RRMSE). The same model function works for different coding scenarios (including scalable and non-scalable video, temporal prediction using either hierarchical B or IPPP structure, etc.) with very high accuracy (average PC $>$ 0.99), but the values of model parameters differ. Using the proposed rate model and the quality model introduced in a separate work, we show how to optimize the STAR for a given rate constraint, which is important for both encoder rate control and scalable video adaptation. Furthermore, we demonstrate how to order the spatial, temporal and amplitude layers of a scalable video in a rate-quality optimized way.

Connected through hyperedge HE-1 (induced by source paper "viewport adaptation based immersive video streaming perceptual modeling and applications"):
  with members:
    - "q star a perceptual video quality model considering impact of spatial temporal and amplitude resolutions"

Connected through hyperedge HE-2 (induced by source paper "optimal selection of adaptive streaming representations"):
  with members:
    - "using the buffer to avoid rebuffers evidence from a large video streaming service"
    - "streaming video over http with consistent quality"
    - "probe and adapt rate adaptation for http video streaming at scale"
    - "nova qoe driven optimization of dash based video delivery in networks"

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)