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: "wideband spectrum sensing at sub nyquist rates"
  Abstract: We present a mixed analog-digital spectrum sensing method that is especially suited to the typical wideband setting of cognitive radio (CR). The advantages of our system with respect to current architectures are threefold. First, our analog front-end is fixed and does not involve scanning hardware. Second, both the analog-to-digital conversion (ADC) and the digital signal processing (DSP) rates are substantially below Nyquist. Finally, the sensing resources are shared with the reception path of the CR, so that the lowrate streaming samples can be used for communication purposes of the device, besides the sensing functionality they provide. Combining these advantages leads to a real time map of the spectrum with minimal use of mobile resources. Our approach is based on the modulated wideband converter (MWC) system, which samples sparse wideband inputs at sub-Nyquist rates. We report on results of hardware experiments, conducted on an MWC prototype circuit, which affirm fast and accurate spectrum sensing in parallel to CR communication.

Connected through hyperedge HE-1 (induced by source paper "analog to digital cognitive radio sampling detection and hardware"):
  with members:
    - "sub nyquist cyclostationary detection for cognitive radio"
    - "cascade compressed carrier and doa estimation"
    - "sub nyquist sampling for power spectrum sensing in cognitive radios a unified approach"
    - "compressive and noncompressive power spectral density estimation from periodic nonuniform samples"
    - "noise folding in compressed sensing"
    - "sub nyquist sampling bridging theory and practice"
    - "xampling signal acquisition and processing in union of subspaces"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"

Connected through hyperedge HE-2 (induced by source paper "covariance based ofdm spectrum sensing with sub nyquist samples"):
  with members:
    - "wideband spectrum sensing with sub nyquist sampling in cognitive radios"
    - "xampling signal acquisition and processing in union of subspaces"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

Connected through hyperedge HE-3 (induced by source paper "compressive identification of active ofdm subcarriers in presence of timing offset"):
  with members:
    - "covariance based ofdm spectrum sensing with sub nyquist samples"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"

Connected through hyperedge HE-4 (induced by source paper "sub nyquist sampling for power spectrum sensing in cognitive radios a unified approach"):
  with members:
    - "fourier domain beamforming the path to compressed ultrasound imaging"
    - "sub nyquist radar via doppler focusing"
    - "compressive and noncompressive power spectral density estimation from periodic nonuniform samples"
    - "noise folding in compressed sensing"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "eigenvalue based spectrum sensing algorithms for cognitive radio"
    - "spectrum sensing algorithms for cognitive radio based on statistical covariances"

Connected through hyperedge HE-5 (induced by source paper "subspace recovery from structured union of subspaces"):
  with members:
    - "performance bounds and design criteria for estimating finite rate of innovation signals"
    - "near oracle performance of greedy block sparse estimation techniques from noisy measurements"
    - "structured compressed sensing from theory to applications"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "time delay estimation from low rate samples a union of subspaces approach"
    - "model based compressive sensing"
    - "distributed compressive sensing"
    - "performance analysis for sparse support recovery"

Connected through hyperedge HE-6 (induced by source paper "subspace detection from structured union of subspaces via linear sampling"):
  with members:
    - "performance bounds and design criteria for estimating finite rate of innovation signals"
    - "near oracle performance of greedy block sparse estimation techniques from noisy measurements"
    - "structured compressed sensing from theory to applications"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "model based compressive sensing"
    - "distributed compressive sensing"
    - "performance analysis for sparse support recovery"
    - "necessary and sufficient conditions for sparsity pattern recovery"

Connected through hyperedge HE-7 (induced by source paper "sub nyquist sampling bridging theory and practice"):
  with members:
    - "performance bounds and design criteria for estimating finite rate of innovation signals"
    - "innovation rate sampling of pulse streams with application to ultrasound imaging"
    - "identification of parametric underspread linear systems and super resolution radar"
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "time delay estimation from low rate samples a union of subspaces approach"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "compressed sensing of analog signals in shift invariant spaces"

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.AR(Hardware Architecture)