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: "d oamp a denoising based signal recovery algorithm for compressed sensing"
  Abstract: Approximate message passing (AMP) is an efficient iterative signal recovery algorithm for compressed sensing (CS). For sensing matrices with independent and identically distributed (i.i.d.) Gaussian entries, the behavior of AMP can be asymptotically described by a scaler recursion called state evolution. Orthogonal AMP (OAMP) is a variant of AMP that imposes a divergence-free constraint on the denoiser. In this paper, we extend OAMP to incorporate generic denoisers, hence the name D-OAMP. Our numerical results show that state evolution predicts the performance of D-OAMP well for generic denoisers when i.i.d. Gaussian or partial orthogonal sensing matrices are involved. We compare the performances of denosing-AMP (D-AMP) and D-OAMP for recovering natural images from CS measurements. Simulation results show that D-OAMP outperforms D-AMP in both convergence speed and recovery accuracy for partial orthogonal sensing matrices.

Connected through hyperedge HE-1 (induced by source paper "a unified bayesian inference framework for generalized linear models"):
  with members:
    - "hybrid approximate message passing"
    - "generalized expectation consistent signal recovery for nonlinear measurements"
    - "vector approximate message passing for the generalized linear model"
    - "generalized turbo signal recovery for nonlinear measurements and orthogonal sensing matrices"
    - "denoising based vector approximate message passing"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "efficient high dimensional inference in the multiple measurement vector problem"
    - "expectation maximization gaussian mixture approximate message passing"

Connected through hyperedge HE-2 (induced by source paper "limits on sparse data acquisition ric analysis of finite gaussian matrices"):
  with members:
    - "graded quantization for multiple description coding of compressive measurements"
    - "refined analysis of sparse mimo radar"
    - "operational rate distortion performance of single source and distributed compressed sensing"
    - "sharp rip bound for sparse signal and low rank matrix recovery"
    - "improved bounds on restricted isometry constants for gaussian matrices"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "sparse recovery with orthogonal matching pursuit under rip"
    - "compressed sensing how sharp is the restricted isometry property"

Connected through hyperedge HE-3 (induced by source paper "denoising based turbo compressed sensing"):
  with members:
    - "turbo compressed sensing with partial dft sensing matrix"
    - "near optimal compressed sensing without priors parametric sure approximate message passing"
    - "from denoising to compressed sensing"
    - "expectation maximization gaussian mixture approximate message passing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "message passing algorithms for compressed sensing i motivation and construction"
    - "message passing algorithms for 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)