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: "verifiable and computable performance analysis of sparsity recovery"
  Abstract: Abstract : In this paper, we develop verifiable and computable performance analysis of sparsity recovery. We define a family of goodness measures for arbitrary sensing matrices as a set of optimization problems, and design algorithms with a theoretical global convergence guarantee to compute these goodness measures. The proposed algorithms solve a series of second-order cone programs, or linear programs. As a by-product, we implement an efficient algorithm to verify a sufficient condition for exact sparsity recovery in the noise-free case. We derive performance bounds on the recovery errors in terms of these goodness measures. We also analytically demonstrate that the developed goodness measures are non-degenerate for a large class of random sensing matrices, as long as the number of measurements is relatively large. Numerical experiments show that, compared with the restricted isometry based performance bounds, our error bounds apply to a wider range of problems and are tighter, when the sparsity levels of the signals are relatively low.

Connected through hyperedge HE-1 (induced by source paper "precisely verifying the null space conditions in compressed sensing a sandwiching algorithm"):
  with members:
    - "sparse recovery from nonlinear measurements with applications in bad data detection for power networks"
    - "fixed point theory and semidefinite programming for computable performance analysis of block sparsity recovery"

Connected through hyperedge HE-2 (induced by source paper "sparse recovery from nonlinear measurements with applications in bad data detection for power networks"):
  with members:
    - "on state estimation with bad data detection"
    - "compressed sensing how sharp is the restricted isometry property"
    - "various thresholds for ell_1 optimization in compressed sensing"

Connected through hyperedge HE-3 (induced by source paper "the stability of low rank matrix reconstruction a constrained singular value view"):
  with members:
    - "performance analysis of sparse recovery based on constrained minimal singular values"
    - "fixed point theory and semidefinite programming for computable performance analysis of block sparsity recovery"
    - "tight oracle bounds for low rank matrix recovery from a minimal number of random measurements"
    - "matrix completion with noise"
    - "robust principal component analysis"
    - "exact matrix completion via convex optimization"

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)