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: "proof of convergence and performance analysis for sparse recovery via zero point attracting projection"
  Abstract: A recursive algorithm named zero-point attracting projection (ZAP) is proposed recently for sparse signal reconstruction. Compared with the reference algorithms, ZAP demonstrates rather good performance in recovery precision and robustness. However, any theoretical analysis about the mentioned algorithm, even a proof on its convergence, is not available. In this work, a strict proof on the convergence of ZAP is provided and the condition of convergence is put forward. Based on the theoretical analysis, it is further proved that ZAP is non-biased and can approach the sparse solution to any extent, with the proper choice of step-size. Furthermore, the case of inaccurate measurements in noisy scenario is also discussed. It is proved that disturbance power linearly reduces the recovery precision, which is predictable but not preventable. The reconstruction deviation of -compressible signal is also provided. Finally, numerical simulations are performed to verify the theoretical analysis.

Connected through hyperedge HE-1 (induced by source paper "robustness of sparse recovery via f minimization a topological viewpoint"):
  with members:
    - "living on the edge phase transitions in convex programs with random data"
    - "approximate projected generalized gradient methods with sparsity inducing penalties"
    - "the convergence guarantees of a non convex approach for sparse recovery"
    - "optimal phase transitions in compressed sensing"
    - "compressed sensing how sharp is the restricted isometry property"
    - "a stochastic gradient approach on compressive sensing signal reconstruction based on adaptive filtering framework"
    - "on the stable recovery of the sparsest overcomplete representations in presence of noise"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"

Connected through hyperedge HE-2 (induced by source paper "an improved variable step size zero point attracting projection algorithm"):
  with members:
    - "a new variable step size zero point attracting projection algorithm"
    - "performance analysis of l_0 norm constraint least mean square algorithm"
    - "a stochastic gradient approach on compressive sensing signal reconstruction based on adaptive filtering framework"
    - "l_ 0 norm constraint lms algorithm for sparse system identification"

Connected through hyperedge HE-3 (induced by source paper "on the performance bound of sparse estimation with sensing matrix perturbation"):
  with members:
    - "perturbation analysis of orthogonal matching pursuit"
    - "the pros and cons of compressive sensing for wideband signal acquisition noise folding versus dynamic range"
    - "sparsity cognizant total least squares for perturbed compressive sampling"
    - "noise folding in compressed sensing"
    - "how well can we estimate a sparse vector"
    - "general deviants an analysis of perturbations in compressed sensing"
    - "compressed sensing how sharp is the restricted isometry property"
    - "a stochastic gradient approach on compressive sensing signal reconstruction based on adaptive filtering framework"

Connected through hyperedge HE-4 (induced by source paper "oracle order recovery performance of greedy pursuits with replacement against general perturbations"):
  with members:
    - "perturbation analysis of orthogonal matching pursuit"
    - "sparsity cognizant total least squares for perturbed compressive sampling"
    - "improved bounds on restricted isometry constants for gaussian matrices"
    - "general deviants an analysis of perturbations in compressed sensing"
    - "compressed sensing how sharp is the restricted isometry property"
    - "compressed sensing performance bounds under poisson noise"
    - "a stochastic gradient approach on compressive sensing signal reconstruction based on adaptive filtering framework"
    - "new bounds for restricted isometry constants"

Connected through hyperedge HE-5 (induced by source paper "a new variable step size zero point attracting projection algorithm"):
  with members:
    - "performance analysis of l_0 norm constraint least mean square algorithm"
    - "a stochastic gradient approach on compressive sensing signal reconstruction based on adaptive filtering framework"
    - "l_ 0 norm constraint lms algorithm for sparse system identification"

Connected through hyperedge HE-6 (induced by source paper "the convergence guarantees of a non convex approach for sparse recovery"):
  with members:
    - "robustness of sparse recovery via f minimization a topological viewpoint"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "sparse recovery using smoothed ell 0 sl0 convergence analysis"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"
    - "a stochastic gradient approach on compressive sensing signal reconstruction based on adaptive filtering framework"
    - "from theory to practice sub nyquist sampling of sparse wideband analog signals"
    - "a fast approach for overcomplete sparse decomposition based on smoothed ell 0 norm"
    - "subspace pursuit for compressive sensing signal reconstruction"

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)