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: "multichannel sampling of pulse streams at the rate of innovation"
  Abstract: We consider minimal-rate sampling schemes for infinite streams of delayed and weighted versions of a known pulse shape. The minimal sampling rate for these parametric signals is referred to as the rate of innovation and is equal to the number of degrees of freedom per unit time. Although sampling of infinite pulse streams was treated in previous works, either the rate of innovation was not achieved, or the pulse shape was limited to Diracs. In this paper we propose a multichannel architecture for sampling pulse streams with arbitrary shape, operating at the rate of innovation. Our approach is based on modulating the input signal with a set of properly chosen waveforms, followed by a bank of integrators. This architecture is motivated by recent work on sub-Nyquist sampling of multiband signals. We show that the pulse stream can be recovered from the proposed minimal-rate samples using standard tools taken from spectral estimation in a stable way even at high rates of innovation. In addition, we address practical implementation issues, such as reduction of hardware complexity and immunity to failure in the sampling channels. The resulting scheme is flexible and exhibits better noise robustness than previous approaches.

Connected through hyperedge HE-1 (induced by source paper "sub nyquist sar via fourier domain range doppler processing"):
  with members:
    - "fast compressed sensing sar imaging based on approximated observation"
    - "recovery of sparse matrices via matrix sketching"
    - "fourier domain beamforming the path to compressed ultrasound imaging"
    - "xampling at the rate of innovation"
    - "compressed beamforming in ultrasound imaging"
    - "sub nyquist radar via doppler focusing"
    - "sub nyquist sampling bridging theory and practice"

Connected through hyperedge HE-2 (induced by source paper "high resolution compressed sensing radar using difference set codes"):
  with members:
    - "sub nyquist radar via doppler focusing"
    - "innovation rate sampling of pulse streams with application to ultrasound imaging"
    - "structured compressed sensing from theory to applications"
    - "identification of parametric underspread linear systems and super resolution radar"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "a tdoa technique with super resolution based on the volume cross correlation function"):
  with members:
    - "time delay estimation from low rate samples a union of subspaces approach"

Connected through hyperedge HE-4 (induced by source paper "sub nyquist sampling and fourier domain beamforming in volumetric ultrasound imaging"):
  with members:
    - "fourier domain beamforming the path to compressed ultrasound imaging"
    - "compressed beamforming in ultrasound imaging"
    - "innovation rate sampling of pulse streams with application to ultrasound imaging"

Connected through hyperedge HE-5 (induced by source paper "robust spectral compressed sensing via structured matrix completion"):
  with members:
    - "incoherence optimal matrix completion"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "spectral compressed sensing via structured matrix completion"
    - "near minimax line spectral estimation"
    - "compressive demodulation of mutually interfering signals"
    - "compressed sensing off the grid"
    - "super resolution from noisy data"
    - "towards a mathematical theory of super resolution"

Connected through hyperedge HE-6 (induced by source paper "channel capacity under sub nyquist nonuniform sampling"):
  with members:
    - "minimax capacity loss under sub nyquist universal sampling"
    - "shannon meets nyquist capacity of sampled gaussian channels"
    - "channel capacity under general nonuniform sampling"
    - "optimal phase transitions in compressed sensing"
    - "sub nyquist sampling bridging theory and practice"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "increased capacity per unit cost by oversampling"

Connected through hyperedge HE-7 (induced by source paper "the cognitive compressive sensing problem"):
  with members:
    - "compressive link acquisition in multiuser communications"
    - "sub nyquist sampling of short pulses"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "multi channel opportunistic access a case of restless bandits with multiple plays"
    - "on myopic sensing for multi channel opportunistic access structure optimality and performance"
    - "algorithms for dynamic spectrum access with learning for cognitive radio"

Connected through hyperedge HE-8 (induced by source paper "minimax capacity loss under sub nyquist universal sampling"):
  with members:
    - "distortion rate function of sub nyquist sampled gaussian sources"
    - "sampling rate distortion"
    - "backing off from infinity performance bounds via concentration of spectral measure for random mimo channels"
    - "channel capacity under sub nyquist nonuniform sampling"
    - "shannon meets nyquist capacity of sampled gaussian channels"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "living at the edge a large deviations approach to the outage mimo capacity"
    - "optimal phase transitions in compressed sensing"

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)