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 "compressive detection of random subspace signals"):
  Abstract: The problem of compressive detection of random subspace signals is studied. We consider signals modeled as   ${\mathbf{s}}={\mathbf{H}}{\mathbf{x}}$   where   ${\mathbf{H}}$   is an    $N\times K$   matrix with    $K\leq N$   and    ${\mathbf{x}}\sim \mathcal{N}\left({\mathbf{0}}_{K,1},{\sigma }_{x}^{2}{{\mathbf{I}}}_{K}\right)$   . We say that signal   ${\mathbf{s}}$    lies in or leans toward a subspace if the largest eigenvalue of    ${\mathbf{H}}{{\mathbf{H}}}^{T}$   is strictly greater than its smallest eigenvalue. We first design a measurement matrix   ${\mathbf{\Phi}} ={\left[{{\mathbf{\Phi}} }_{s}^{T},{{\mathbf{\Phi}} }_{o}^{T}\right]}^{T}$   comprising of two sub-matrices   ${{\mathbf{\Phi}} }_{s}$   and   ${{\mathbf{\Phi}} }_{o}$   where    ${{\mathbf{\Phi}} }_{s}$   projects the signal to the strongest left-singular vectors, i.e., the left-singular vectors corresponding to the largest singular values, of subspace matrix   ${\mathbf{H}}$   and    ${{\mathbf{\Phi}} }_{o}$   projects it to the weakest left-singular vectors. We then propose two detectors that work based on the difference in energies of the samples measured by the two sub-matrices   ${{\mathbf{\Phi}} }_{s}$    and   ${{\mathbf{\Phi}} }_{o}$   and provide theoretical proofs for their optimality. Simplified versions of the proposed detectors for the case when the variance of noise is known are also provided. Furthermore, we study the performance of the detector when measurements are imprecise and show how imprecision can be compensated by employing more measurement devices. The problem is then re-formulated for the generalized case when the signal lies in the union of a finite number of linear subspaces instead of a single linear subspace. Finally, we study the performance of the proposed methods by simulation examples.

Includes members:
  Member "covariance based ofdm spectrum sensing with sub nyquist samples":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "context aware decentralized invariant signaling for opportunistic communications")
  Member "compressed beamforming in ultrasound imaging":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "serial quantization for sparse time sequences")
  Member "sub nyquist radar via doppler focusing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "sampling and super resolution of sparse signals beyond the fourier domain")
  Member "measurement design for detecting sparse signals":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "measurement matrix design for compressive detection with secrecy guarantees")
  Member "xampling signal acquisition and processing in union of subspaces":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "fast compressed power spectrum estimation towards a practical solution for wideband spectrum sensing")
  Member "measurement matrix design for compressive sensing based mimo radar":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "compressed sensing for wireless communications useful tips and tricks")
  Member "beyond nyquist efficient sampling of sparse bandlimited signals":
    also connected through other hyperedges:
      - Hyperedge HE-7 (induced by source paper "secure and efficient compressed sensing based encryption with sparse matrices")
  Member "from theory to practice sub nyquist sampling of sparse wideband analog signals":
    also connected through other hyperedges:
      - Hyperedge HE-8 (induced by source paper "image guided depth sampling and reconstruction")

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)