human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "sub nyquist sampling for power spectrum sensing in cognitive radios a unified approach"):
  Abstract: In light of the ever-increasing demand for new spectral bands and the underutilization of those already allocated, the concept of Cognitive Radio (CR) has emerged. Opportunistic users could exploit temporarily vacant bands after detecting the absence of activity of their owners. One of the crucial tasks in the CR cycle is therefore spectrum sensing and detection which has to be precise and efficient. Yet, CRs typically deal with wideband signals whose Nyquist rates are very high. In this paper, we propose to reconstruct the power spectrum of such signals from sub-Nyquist samples, rather than the signal itself as done in previous work, in order to perform detection. We consider both sparse and non sparse signals as well as blind and non blind detection in the sparse case. For each one of those scenarios, we derive the minimal sampling rate allowing perfect reconstruction of the signal's power spectrum in a noise-free environment and provide power spectrum recovery techniques that achieve those rates. The analysis is performed for two different signal models considered in the literature, which we refer to as the analog and digital models, and shows that both lead to similar results. Simulations demonstrate power spectrum recovery at the minimal rate in noise-free settings and the impact of several parameters on the detector performance, including signal-to-noise ratio, sensing time and sampling rate.

Includes members:
  Member "fourier domain beamforming the path to compressed ultrasound imaging":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "sparse convolutional beamforming for ultrasound imaging")
  Member "sub nyquist radar via doppler focusing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "sampling and super resolution of sparse signals beyond the fourier domain")
  Member "compressive and noncompressive power spectral density estimation from periodic nonuniform samples":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "analog to digital cognitive radio sampling detection and hardware")
  Member "noise folding in compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "an analysis of noise folding for low rank matrix recovery")
  Member "wideband spectrum sensing at sub nyquist rates":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "analog to digital cognitive radio sampling detection and hardware")
  Member "from theory to practice sub nyquist sampling of sparse wideband analog signals":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "image guided depth sampling and reconstruction")
  Member "eigenvalue based spectrum sensing algorithms for cognitive radio":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "compressive spectrum sensing for cognitive radio networks")
  Member "spectrum sensing algorithms for cognitive radio based on statistical covariances":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "unified analysis of cooperative spectrum sensing over composite and generalized fading channels")

Question: Please tell me which class the source paper 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)