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: "system approximations and generalized measurements in modern sampling theory"
  Abstract: This chapter studies several aspects of signal reconstruction of sampled data in spaces of bandlimited functions. In the first part, signal spaces are characterized in which the classical sampling series uniformly converge, and we investigate whether adaptive recovery algorithms can yield uniform convergence in spaces where non-adaptive sampling series does not. In particular, it is shown that the investigation of adaptive signal recovery algorithms needs completely new analytic tools since the methods used for nonadaptive reconstruction procedures, which are based on the celebrated Banach–Steinhaus theorem, are not applicable in the adaptive case.The second part analyzes the approximation of the output of stable linear time-invariant (LTI) systems based on samples of the input signal, and where the input is assumed to belong to the Paley–Wiener space of bandlimited functions with absolute integrable Fourier transform. If the samples are acquired by point evaluations of the input signal f, then there exist stable LTI systems H such that the approximation process does not converge to the desired output Hf even if the oversampling factor is arbitrarily large. If one allows generalized measurements of the input signal, then the output of every stable LTI system can be uniformly approximated in terms of generalized measurements of the input signal.The last section studies the situation where only the amplitudes of the signal samples are known. It is shown that one can find specific measurement functionals such that signal recovery of bandlimited signals from amplitude measurement is possible, with an overall sampling rate of four times the Nyquist rate.

Connected through hyperedge HE-1 (induced by source paper "strong divergence for system approximations"):
  with members:
    - "signal and system approximation from general measurements"

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)