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: "harmonic analysis and qualitative uncertainty principle"
  Abstract: This paper investigates the mathematical nature of qualitative uncertainty principle (QUP), which plays an important role in mathematics, physics and engineering fields. Consider a 3-tuple (K, H1, H2) that K: H1 -> H2 is an integral operator. Suppose a signal f in H1, {\Omega}1 and {\Omega}2 are domains on which f, Kf define respectively. Does this signal f vanish if |{\Sigma}(f)|<|{\Omega}1|and|{\Sigma}(Kf)|<|{\Omega}2|? The excesses and deficiencies of integral kernel K({\omega}, t) are found to be greatly related to this general formulation of QUP. The complete point theory of integral kernel is so established to deal with the QUP. This theory addresses the density and linear independence of integral kernels. Some algebraic and geometric properties of complete points are presented. It is shown that the satisfaction of QUP depends on the existence of some complete points. By recognizing complete points of their corresponding integral kernels, the QUP with Fourier transform, Wigner-Ville distribution, Gabor transform and wavelet are studied. It is shown the QUP only holds for good behaved integral operators. An investigation of full violation of QUP shows that L2 space is large for high resolution harmonic analysis. And the invertible linear integral transforms whose kernels are complete in L2 probably lead to the satisfaction of QUP. It indicates the performance limitation of linear integral transforms in harmonic analysis. Two possible ways bypassing uncertainty principle, nonlinear method and sparse representation, are thus suggested. The notion of operator family is developed and is applied to understand remarkable performances of recent sparse representation.

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)