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: "exploiting statistical dependencies in sparse representations for signal recovery"
  Abstract: Signal modeling lies at the core of numerous signal and image processing applications. A recent approach that has drawn considerable attention is sparse representation modeling, in which the signal is assumed to be generated as a combination of a few atoms from a given dictionary. In this work we consider a Bayesian setting and go beyond the classic assumption of independence between the atoms. The main goal of this paper is to introduce a statistical model that takes such dependencies into account and show how this model can be used for sparse signal recovery. We follow the suggestion of two recent works and assume that the sparsity pattern is modeled by a Boltzmann machine, a commonly used graphical model. For general dependency models, exact MAP and MMSE estimation of the sparse representation becomes computationally complex. To simplify the computations, we propose greedy approximations of the MAP and MMSE estimators. We then consider a special case in which exact MAP is feasible, by assuming that the dictionary is unitary and the dependency model corresponds to a certain sparse graph. Exploiting this structure, we develop an efficient message passing algorithm that recovers the underlying signal. When the model parameters defining the underlying graph are unknown, we suggest an algorithm that learns these parameters directly from the data, leading to an iterative scheme for adaptive sparse signal recovery. The effectiveness of our approach is demonstrated on real-life signals-patches of natural images-where we compare the denoising performance to that of previous recovery methods that do not exploit the statistical dependencies.

Connected through hyperedge HE-1 (induced by source paper "exploiting restricted boltzmann machines and deep belief networks in compressed sensing"):
  with members:
    - "approximate message passing with restricted boltzmann machine priors"
    - "sparse approximation based on a random overcomplete basis"
    - "variational bayesian algorithm for quantized compressed sensing"
    - "cramer rao type bounds for sparse bayesian learning"
    - "bayesian fusion of multi band images"
    - "structured compressed sensing from theory to applications"
    - "block sparse signals uncertainty relations and efficient recovery"

Connected through hyperedge HE-2 (induced by source paper "multi focus image fusion via coupled sparse representation and dictionary learning"):
  with members:
    - "image fusion with cosparse analysis operator"
    - "plug and play admm for image restoration fixed point convergence and applications"

Connected through hyperedge HE-3 (induced by source paper "spectrum sharing radar coexistence via xampling"):
  with members:
    - "analog to digital cognitive radio sampling detection and hardware"
    - "performance of time delay estimation in a cognitive radar"
    - "sub nyquist cyclostationary detection for cognitive radio"
    - "sub nyquist channel estimation over ieee 802 11ad link"
    - "on the co existence of td lte and radar over 3 5 ghz band an experimental study"
    - "spectrum sharing between a surveillance radar and secondary wi fi networks"
    - "recursive recovery of sparse signal sequences from compressive measurements a review"
    - "sub nyquist sampling for power spectrum sensing in cognitive radios a unified approach"

Connected through hyperedge HE-4 (induced by source paper "two dimensional pattern coupled sparse bayesian learning via generalized approximate message passing"):
  with members:
    - "an empirical bayes approach to recovering linearly constrained non negative sparse signals"
    - "pattern coupled sparse bayesian learning for recovery of block sparse signals"
    - "expectation maximization gaussian mixture approximate message passing"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "model based compressive sensing"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "message passing algorithms for compressed sensing i motivation and construction"

Connected through hyperedge HE-5 (induced by source paper "exploiting prior knowledge in compressed sensing wireless ecg systems"):
  with members:
    - "variational bayesian algorithm for quantized compressed sensing"
    - "cramer rao type bounds for sparse bayesian learning"
    - "theory and applications of compressed sensing"
    - "structured compressed sensing from theory to applications"
    - "sparse signal reconstruction via iterative support detection"
    - "model based compressive sensing"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "greedy sparsity promoting algorithms for distributed learning"):
  with members:
    - "sparsity aware learning and compressed sensing an overview"
    - "sparse distributed learning based on diffusion adaptation"
    - "diffusion adaptation over networks"
    - "mapreduce is good enough if all you have is a hammer throw away everything that s not a nail"
    - "a sparsity promoting adaptive algorithm for distributed learning"
    - "distributed sparse signal recovery for sensor networks"
    - "subspace pursuit for compressive sensing signal reconstruction"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-7 (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"
    - "structured compressed sensing from theory to applications"
    - "on mmse and map denoising under sparse representation modeling over a unitary dictionary"
    - "subspace pursuit for compressive sensing signal reconstruction"

Connected through hyperedge HE-8 (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"
    - "structured compressed sensing from theory to applications"
    - "on mmse and map denoising under sparse representation modeling over a unitary dictionary"
    - "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)