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: "discrete sampling and interpolation universal sampling sets for discrete bandlimited spaces"
  Abstract: We study the problem of interpolating all values of a discrete signal f of length N when d <; N values are known, especially in the case when the Fourier transform of the signal is zero outside some prescribed index set J; these comprise the (generalized) bandlimited spaces BJ. The sampling pattern for f is specified by an index set I, and is said to be a universal sampling set if samples in the locations I can be used to interpolate signals from BJfor any J. When N is a prime power we give several characterizations of universal sampling sets, some structure theorems for such sets, an algorithm for their construction, and a formula that counts them. There are also natural applications to additive uncertainty principles.

Connected through hyperedge HE-1 (induced by source paper "convolution idempotents with a given zero set"):
  with members:
    - "discrete sampling a graph theoretic approach to orthogonal interpolation"

Connected through hyperedge HE-2 (induced by source paper "universal spatiotemporal sampling sets for discrete spatially invariant evolution processes"):
  with members:
    - "system identification in dynamical sampling"
    - "exact reconstruction of signals in evolutionary systems via spatiotemporal trade off"
    - "spatially distributed sampling and reconstruction"
    - "sparse recovery from combined fusion frame measurements"

Connected through hyperedge HE-3 (induced by source paper "sampling and distortion tradeoffs for bandlimited periodic signals"):
  with members:
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "sampling rate distortion"
    - "the emerging field of signal processing on graphs extending high dimensional data analysis to networks and other irregular domains"
    - "sample distortion for compressed imaging"
    - "time stampless adaptive nonuniform sampling for stochastic signals"
    - "optimal sampling points in reproducing kernel hilbert spaces"
    - "the sampling rate distortion tradeoff for sparsity pattern recovery in compressed sensing"

Connected through hyperedge HE-4 (induced by source paper "discrete sampling and interpolation orthogonal interpolation for discrete bandlimited signals"):
  with members:
    - "compressed sensing matrices from fourier matrices"

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)