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: "performance bounds and design criteria for estimating finite rate of innovation signals"
  Abstract: In this paper, we consider the problem of estimating finite rate of innovation (FRI) signals from noisy measurements, and specifically analyze the interaction between FRI techniques and the underlying sampling methods. We first obtain a fundamental limit on the estimation accuracy attainable regardless of the sampling method. Next, we provide a bound on the performance achievable using any specific sampling approach. Essential differences between the noisy and noise-free cases arise from this analysis. In particular, we identify settings in which noise-free recovery techniques deteriorate substantially under slight noise levels, thus quantifying the numerical instability inherent in such methods. This instability, which is only present in some families of FRI signals, is shown to be related to a specific type of structure, which can be characterized by viewing the signal model as a union of subspaces. Finally, we develop a methodology for choosing the optimal sampling kernels for linear reconstruction, based on a generalization of the Karhunen-Loeve transform. The results are illustrated for several types of time-delay estimation problems.

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"
    - "xampling at the rate of innovation"
    - "xampling signal acquisition and processing in union of subspaces"

Connected through hyperedge HE-2 (induced by source paper "on the snr variability in noisy compressed sensing"):
  with members:
    - "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range"
    - "regime change bit depth versus measurement rate in compressive sensing"
    - "noise folding in compressed sensing"
    - "information theoretic bounds for compressed sensing"

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

Connected through hyperedge HE-4 (induced by source paper "compressive parameter estimation in awgn"):
  with members:
    - "near minimax line spectral estimation"
    - "atomic norm denoising with applications to line spectral estimation"
    - "towards a mathematical theory of super resolution"
    - "coherence pattern guided compressive sensing with unresolved grids"
    - "noise folding in compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "subspace recovery from structured union of subspaces"):
  with members:
    - "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"
    - "wideband spectrum sensing at sub nyquist rates"
    - "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:
    - "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"
    - "wideband spectrum sensing at sub nyquist rates"
    - "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 "xampling at the rate of innovation"):
  with members:
    - "innovation rate sampling of pulse streams with application to ultrasound imaging"
    - "xampling compressed sensing of analog signals"
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "xampling signal acquisition and processing in union of subspaces"
    - "time delay estimation from low rate samples a union of subspaces approach"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "sampling and reconstructing signals from a union of linear subspaces"

Connected through hyperedge HE-8 (induced by source paper "structured compressed sensing from theory to applications"):
  with members:
    - "exploiting statistical dependencies in sparse representations for signal recovery"
    - "innovation rate sampling of pulse streams with application to ultrasound imaging"
    - "near oracle performance of greedy block sparse estimation techniques from noisy measurements"
    - "xampling in ultrasound imaging"
    - "identification of parametric underspread linear systems and super resolution radar"
    - "multichannel sampling of pulse streams at the rate of innovation"
    - "noise folding in compressed sensing"
    - "general deviants an analysis of perturbations 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)