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: "matched filtering from limited frequency samples"
  Abstract: In this paper, we study a simple correlation-based strategy for estimating the unknown delay and amplitude of a signal based on a small number of noisy, randomly chosen frequency-domain samples. We model the output of this “compressive matched filter” as a random process whose mean equals the scaled, shifted autocorrelation function of the template signal. Using tools from the theory of empirical processes, we prove that the expected maximum deviation of this process from its mean decreases sharply as the number of measurements increases, and we also derive a probabilistic tail bound on the maximum deviation. Putting all of this together, we bound the minimum number of measurements required to guarantee that the empirical maximum of this random process occurs sufficiently close to the true peak of its mean function. We conclude that for broad classes of signals, this compressive matched filter will successfully estimate the unknown delay (with high probability and within a prescribed tolerance) using a number of random frequency-domain samples that scales inversely with the signal-to-noise ratio and only logarithmically in the observation bandwidth and the possible range of delays.

Connected through hyperedge HE-1 (induced by source paper "compressive parameter estimation via k median clustering"):
  with members:
    - "robust recovery of complex exponential signals from random gaussian projections via low rank hankel matrix reconstruction"
    - "compressive parameter estimation for sparse translation invariant signals using polar interpolation"
    - "robust spectral compressed sensing via structured matrix completion"
    - "spectral compressive sensing with polar interpolation"
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "atomic norm denoising with applications to line spectral estimation"
    - "compressed sensing off the grid"
    - "super resolution from noisy data"

Connected through hyperedge HE-2 (induced by source paper "compressive phase only filtering at extreme compression rates"):
  with members:
    - "efficient adaptation of complex valued noiselet sensing matrices for compressed single pixel imaging"
    - "can compressed sensing beat the nyquist sampling rate"

Connected through hyperedge HE-3 (induced by source paper "greed is super a fast algorithm for super resolution"):
  with members:
    - "phase retrieval via wirtinger flow theory and algorithms"
    - "music for single snapshot spectral estimation stability and super resolution"
    - "spectral compressive sensing with polar interpolation"
    - "near minimax line spectral estimation"
    - "super resolution via superset selection and pruning"
    - "a tensor based method for missing traffic data completion"
    - "support detection in super resolution"
    - "super resolution from noisy data"

Connected through hyperedge HE-4 (induced by source paper "approximating sampled sinusoids and multiband signals using multiband modulated dpss dictionaries"):
  with members:
    - "compressed sensing off the grid"
    - "towards a mathematical theory of super resolution"
    - "compressive sensing of analog signals using discrete prolate spheroidal sequences"
    - "coherence pattern guided compressive sensing with unresolved grids"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"

Connected through hyperedge HE-5 (induced by source paper "compressed subspace matching on the continuum"):
  with members:
    - "compressive parameter estimation for sparse translation invariant signals using polar interpolation"
    - "dimensionality reduction with subgaussian matrices a unified theory"
    - "compressive parameter estimation in awgn"
    - "near minimax line spectral estimation"
    - "toward a unified theory of sparse dimensionality reduction in euclidean space"
    - "blind deconvolution using convex programming"
    - "stable manifold embeddings with structured random matrices"
    - "sparse recovery from combined fusion frame measurements"

Connected through hyperedge HE-6 (induced by source paper "new analysis of manifold embeddings and signal recovery from compressive measurements"):
  with members:
    - "the restricted isometry property for random block diagonal matrices"
    - "signal recovery on incoherent manifolds"
    - "stable manifold embeddings with structured random matrices"
    - "approximation of points on low dimensional manifolds via random linear projections"
    - "measurement bounds for sparse signal ensembles via graphical models"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "restricted isometries for partial random circulant matrices"
    - "circulant and toeplitz matrices 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)