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: "xampling at the rate of innovation"
  Abstract: We address the problem of recovering signals from samples taken at their rate of innovation. Our only assumption is that the sampling system is such that the parameters defining the signal can be stably determined from the samples, a condition that lies at the heart of every sampling theorem. Consequently, our analysis subsumes previously studied nonlinear acquisition devices and nonlinear signal classes. In particular, we do not restrict attention to memoryless nonlinear distortions or to union-of-subspace models. This allows treatment of various finite-rate-of-innovation (FRI) signals that were not previously studied, including, for example, continuous phase modulation transmissions. Our strategy relies on minimizing the error between the measured samples and those corresponding to our signal estimate. This least-squares (LS) objective is generally nonconvex and might possess many local minima. Nevertheless, we prove that under the stability hypothesis, any optimization method designed to trap a stationary point of the LS criterion necessarily converges to the true solution. We demonstrate our approach in the context of recovering pulse streams in settings that were not previously treated. Furthermore, in situations for which other algorithms are applicable, we show that our method is often preferable in terms of noise robustness.

Connected through hyperedge HE-1 (induced by source paper "joint transmit and receive filter optimization for sub nyquist delay doppler estimation"):
  with members:
    - "analog transmit signal optimization for undersampled delay doppler estimation"
    - "performance bounds and design criteria for estimating finite rate of innovation signals"
    - "xampling signal acquisition and processing in union of subspaces"

Connected through hyperedge HE-2 (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"
    - "compressed beamforming in ultrasound imaging"
    - "sub nyquist radar via doppler focusing"
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "sub nyquist sampling bridging theory and practice"

Connected through hyperedge HE-3 (induced by source paper "joint transmit and receive filter optimization for sub nyquist wireless channel estimation"):
  with members:
    - "performance bounds and design criteria for estimating finite rate of innovation signals"
    - "xampling signal acquisition and processing in union of subspaces"

Connected through hyperedge HE-4 (induced by source paper "super resolution in phase space"):
  with members:
    - "super resolution extremal functions and the condition number of vandermonde matrices"
    - "super resolution via superset selection and pruning"
    - "towards a mathematical theory of super resolution"
    - "time delay estimation from low rate samples a union of subspaces approach"

Connected through hyperedge HE-5 (induced by source paper "fourier domain beamforming the path to compressed ultrasound imaging"):
  with members:
    - "fourier domain beamforming for medical ultrasound"
    - "compressed beamforming in ultrasound imaging"
    - "innovation rate sampling of pulse streams with application to ultrasound imaging"
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "xampling signal acquisition and processing in union of subspaces"

Connected through hyperedge HE-6 (induced by source paper "performance bounds and design criteria for estimating finite rate of innovation signals"):
  with members:
    - "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"
    - "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"

Connected through hyperedge HE-7 (induced by source paper "compressed beamforming in ultrasound imaging"):
  with members:
    - "innovation rate sampling of pulse streams with application to ultrasound imaging"
    - "xampling in ultrasound imaging"
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "xampling signal acquisition and processing in union of subspaces"

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)