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: "on mmse and map denoising under sparse representation modeling over a unitary dictionary"
  Abstract: Among the many ways to model signals, a recent approach that draws considerable attention is sparse representation modeling. In this model, the signal is assumed to be generated as a random linear combination of a few atoms from a prespecified dictionary. In this work, two Bayesian denoising algorithms are analyzed for this model-the maximum a posteriori probability (MAP) and the minimum-mean-squared-error (MMSE) estimators, both under the assumption that the dictionary is unitary. It is well known that both these estimators lead to a scalar shrinkage on the transformed coefficients, albeit with a different response curve. We derive explicit expressions for the estimation-error for these two estimators. Upper bounds on these errors are developed and tied to the expected error of the so-called oracle estimator, for which the support is assumed to be known. This analysis establishes a worst-case gain-factor between the MAP/MMSE estimation errors and that of the oracle.

Connected through hyperedge HE-1 (induced by source paper "power constrained sparse gaussian linear dimensionality reduction over noisy channels"):
  with members:
    - "joint source channel vector quantization for compressed sensing"
    - "channel optimized vector quantizer design for compressed sensing measurements"
    - "estimating unknown sparsity in compressed sensing"
    - "projection based and look ahead strategies for atom selection"
    - "exploiting statistical dependencies in sparse representations for signal recovery"
    - "structured compressed sensing from theory to applications"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-2 (induced by source paper "sparse gaussian linear dimensionality reduction over noisy communication channels"):
  with members:
    - "joint source channel vector quantization for compressed sensing"
    - "estimating unknown sparsity in compressed sensing"
    - "exploiting statistical dependencies in sparse representations for signal recovery"
    - "structured compressed sensing from theory to applications"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-3 (induced by source paper "exploiting statistical dependencies in sparse representations for signal recovery"):
  with members:
    - "partially linear estimation with application to sparse signal recovery from measurement pairs"
    - "structured compressed sensing from theory to applications"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "model based compressive sensing"
    - "message passing algorithms for compressed sensing i motivation and construction"
    - "bayesian compressive sensing via belief propagation"

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.CV(Computer Vision and Pattern Recognition)