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: "colored gaussian multiple descriptions spectral domain characterization and time domain design"
  Abstract: It is well known that Shannon’s rate-distortion function (R DF) in the colored quadratic Gaussian (QG) case, can be parametrized via a single Lagrangian variable (the “water level” in the reverse water filling solution). In this work, we show that the symmetric co lored QG multiple-description (MD) RDF in the case of two descriptions, can be parametrized via two Lagrangian variables. To establish this result, we use two key ideas. First, we propose a new representation for the MD test channel, and show that the minimum mutual information rate across this channel coincide with the QG MD RDF. Second, we use variational calculus to obtain a spectral domain repres entation of the test channel’s optimal side and central distortion spectra given the source spectral densi ty and the side and central distortion constraints. The distortion spectra are specified via two Lagrangian para meters, which control the trade-off between the side distortion, the central distortion, and the coding rate. We also show that the symmetric colored QG MD RDF can be achieved by noise-shaped predictive coding, dithered quantization, and memoryless entropy coding. In particular, we show that the proposed MD test channel can be materialized by embedding two source prediction loops, one for each description, within a common noise shaping loop whose parameters are explicitly found from the spectral-domain characterization. The source prediction loops exploit the source memory, and thus reduce the coding rate. The noise-shaping loop, controls the trade-off between the side and the central distortions by sh aping the quantization noise.

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.IT(Information Theory)