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: "efficient sampling of sparse wideband analog signals"
  Abstract: Periodic nonuniform sampling is a known method to sample spectrally sparse signals below the Nyquist rate. This strategy relies on the implicit assumption that the individual samplers are exposed to the entire frequency range. This assumption becomes impractical for wideband sparse signals. The current paper proposes an alternative sampling stage that does not require a full-band front end. Instead, signals are captured with an analog front end that consists of a bank of multipliers and lowpass filters whose cutoff is much lower than the Nyquist rate. The problem of recovering the original signal from the low-rate samples can be studied within the framework of compressive sampling. An appropriate parameter selection ensures that the samples uniquely determine the analog input. Moreover, the analog input can be stably reconstructed with digital algorithms. Numerical experiments support the theoretical analysis.

Connected through hyperedge HE-1 (induced by source paper "zero point attracting projection algorithm for sequential compressive sensing"):
  with members:
    - "a stochastic gradient approach on compressive sensing signal reconstruction based on adaptive filtering framework"

Connected through hyperedge HE-2 (induced by source paper "from theory to practice sub nyquist sampling of sparse wideband analog signals"):
  with members:
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "uncertainty relations for shift invariant analog signals"
    - "compressed sensing of analog signals in shift invariant spaces"
    - "average case analysis of multichannel sparse recovery using convex relaxation"
    - "multirate synchronous sampling of sparse multiband signals"

Connected through hyperedge HE-3 (induced by source paper "beyond nyquist efficient sampling of sparse bandlimited signals"):
  with members:
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "circulant and toeplitz matrices in compressed sensing"
    - "compressed sensing of analog signals in shift invariant spaces"

Connected through hyperedge HE-4 (induced by source paper "non uniform sampling and reconstruction of multi band signals and its application in wideband spectrum sensing of cognitive radio"):
  with members:
    - "eigenvalue based spectrum sensing algorithms for cognitive radio"

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)