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: "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.

Connected through hyperedge HE-1 (induced by source paper "fast compressed power spectrum estimation towards a practical solution for wideband spectrum sensing"):
  with members:
    - "efficient spectrum availability information recovery for wideband dsa networks a weighted compressive sampling approach"
    - "wideband spectrum sensing for cognitive radio networks a survey"
    - "wideband spectrum sensing with sub nyquist sampling in cognitive radios"
    - "xampling signal acquisition and processing in union of subspaces"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

Connected through hyperedge HE-2 (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"
    - "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"
    - "wideband spectrum sensing at sub nyquist rates"

Connected through hyperedge HE-3 (induced by source paper "an active sequential xampling detector for spectrum sensing"):
  with members:
    - "utility maximizing sequential sensing over a finite horizon"
    - "converse bounds for noisy group testing with arbitrary measurement matrices"
    - "cross layer estimation and control for cognitive radio exploiting sparse network dynamics"
    - "info greedy sequential adaptive compressed sensing"
    - "wideband spectrum sensing with sub nyquist sampling in cognitive radios"
    - "boolean compressed sensing and noisy group testing"
    - "on optimality of myopic policy for restless multi armed bandit problem an axiomatic approach"
    - "compressive and noncompressive power spectral density estimation from periodic nonuniform samples"

Connected through hyperedge HE-4 (induced by source paper "sub nyquist cyclostationary detection for cognitive radio"):
  with members:
    - "noise folding in compressed sensing"
    - "sub nyquist sampling bridging theory and practice"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"

Connected through hyperedge HE-5 (induced by source paper "spectrum sharing radar coexistence via xampling"):
  with members:
    - "analog to digital cognitive radio sampling detection and hardware"
    - "performance of time delay estimation in a cognitive radar"
    - "sub nyquist cyclostationary detection for cognitive radio"
    - "sub nyquist channel estimation over ieee 802 11ad link"
    - "on the co existence of td lte and radar over 3 5 ghz band an experimental study"
    - "spectrum sharing between a surveillance radar and secondary wi fi networks"
    - "recursive recovery of sparse signal sequences from compressive measurements a review"
    - "pattern coupled sparse bayesian learning for recovery of block sparse signals"

Connected through hyperedge HE-6 (induced by source paper "positive definite estimation of large covariance matrix using generalized nonconvex penalties"):
  with members:
    - "massive mimo as a big data system random matrix models and testbed"
    - "recovery of sparse matrices via matrix sketching"
    - "sketching sparse matrices"

Connected through hyperedge HE-7 (induced by source paper "compressed sensing for wireless communications useful tips and tricks"):
  with members:
    - "structured compressive sensing based spatio temporal joint channel estimation for fdd massive mimo"
    - "an overview of signal processing techniques for millimeter wave mimo systems"
    - "spatially common sparsity based adaptive channel estimation and feedback for fdd massive mimo"
    - "compressive sensing with prior support quality information and application to massive mimo channel estimation with temporal correlation"
    - "channel estimation and hybrid precoding for millimeter wave cellular systems"
    - "distributed compressive csit estimation and feedback for fdd multi user massive mimo systems"
    - "spatially sparse precoding in millimeter wave mimo systems"
    - "statistical recovery of simultaneously sparse time varying signals from multiple measurement vectors"

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)