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: "analog to digital cognitive radio sampling detection and hardware"
  Abstract: The radio spectrum is the radio-frequency (RF) portion of the electromagnetic spectrum. These spectral resources are traditionally allocated to licensed or primary users (PUs) by governmental organizations. As discussed in "Radio-Frequency Spectral Resources," most of the frequency bands are already allocated to one or more PUs. Consequently, new users cannot easily find free frequency bands. Spurred by the everincreasing demand from new wireless communication applications, this issue has become critical over the past few years.

Connected through hyperedge HE-1 (induced by source paper "fast and robust spectrally sparse signal recovery a provable non convex approach via robust low rank hankel matrix reconstruction"):
  with members:
    - "accelerated alternating projections for robust principal component analysis"
    - "fast and provable algorithms for spectrally sparse signal reconstruction via low rank hankel matrix completion"
    - "a general framework for compressed sensing and parallel mri using annihilating filter based low rank hankel matrix"
    - "robust spectral compressed sensing via structured matrix completion"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "fixed rank matrix factorizations and riemannian low rank optimization"
    - "compressed sensing and matrix completion with constant proportion of corruptions"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"

Connected through hyperedge HE-2 (induced by source paper "the capacity of memoryless channels with sampled cyclostationary gaussian noise"):
  with members:
    - "on the capacity of mimo broadband power line communications channels"
    - "sub nyquist cyclostationary detection for cognitive radio"
    - "on the capacity of narrowband plc channels"
    - "channel capacity under sub nyquist nonuniform sampling"
    - "frequency shift filtering for ofdm signal recovery in narrowband power line communications"
    - "what will 5g be"
    - "shannon meets nyquist capacity of sampled gaussian channels"
    - "on the minimax capacity loss under sub nyquist universal sampling"

Connected through hyperedge HE-3 (induced by source paper "low rank toeplitz matrix estimation via random ultra sparse rulers"):
  with members:
    - "sparse doppler sensing based on nested arrays"
    - "exact and stable covariance estimation from quadratic sampling via convex programming"
    - "nearly optimal sparse fourier transform"

Connected through hyperedge HE-4 (induced by source paper "multi player bandits a trekking approach"):
  with members:
    - "multi player bandits a musical chairs approach"
    - "concurrent bandits and cognitive radio networks"
    - "further optimal regret bounds for thompson sampling"
    - "distributed algorithms for learning and cognitive medium access with logarithmic regret"
    - "cognitive medium access exploration exploitation and competition"

Connected through hyperedge HE-5 (induced by source paper "sub nyquist radar principles and prototypes"):
  with members:
    - "recovery of sparse matrices via matrix sketching"
    - "expected rip conditioning of the modulated wideband converter"

Connected through hyperedge HE-6 (induced by source paper "spectrum sharing radar coexistence via xampling"):
  with members:
    - "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"
    - "sub nyquist sampling for power spectrum sensing in cognitive radios a unified approach"
    - "pattern coupled sparse bayesian learning for recovery of block sparse signals"

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.CE(Computational Engineering, Finance, and Science)