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: "pattern coupled sparse bayesian learning for recovery of block sparse signals"
  Abstract: We consider the problem of recovering block-sparse signals whose structures are unknown \emph{a priori}. Block-sparse signals with nonzero coefficients occurring in clusters arise naturally in many practical scenarios. However, the knowledge of the block structure is usually unavailable in practice. In this paper, we develop a new sparse Bayesian learning method for recovery of block-sparse signals with unknown cluster patterns. Specifically, a pattern-coupled hierarchical Gaussian prior model is introduced to characterize the statistical dependencies among coefficients, in which a set of hyperparameters are employed to control the sparsity of signal coefficients. Unlike the conventional sparse Bayesian learning framework in which each individual hyperparameter is associated independently with each coefficient, in this paper, the prior for each coefficient not only involves its own hyperparameter, but also the hyperparameters of its immediate neighbors. In doing this way, the sparsity patterns of neighboring coefficients are related to each other and the hierarchical model has the potential to encourage structured-sparse solutions. The hyperparameters, along with the sparse signal, are learned by maximizing their posterior probability via an expectation-maximization (EM) algorithm. Numerical results show that the proposed algorithm presents uniform superiority over other existing methods in a series of experiments.

Connected through hyperedge HE-1 (induced by source paper "learning cluster structured sparsity by reweighting"):
  with members:
    - "trainable ista for sparse signal recovery"
    - "theoretical linear convergence of unfolded ista and its practical weights and thresholds"
    - "ista net interpretable optimization inspired deep network for image compressive sensing"
    - "two dimensional pattern coupled sparse bayesian learning via generalized approximate message passing"
    - "tradeoffs between convergence speed and reconstruction accuracy in inverse problems"
    - "adam a method for stochastic optimization"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-2 (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-3 (induced by source paper "approximate message passing with nearest neighbor sparsity pattern learning"):
  with members:
    - "two dimensional pattern coupled sparse bayesian learning via generalized approximate message passing"
    - "expectation maximization gaussian mixture approximate message passing"
    - "approximate message passing with consistent parameter estimation and applications to sparse learning"
    - "a message passing receiver for bicm ofdm over unknown clustered sparse channels"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "compressive imaging using approximate message passing and a markov tree prior"
    - "block sparse signals uncertainty relations and efficient recovery"
    - "generalized approximate message passing for estimation with random linear mixing"

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"
    - "expectation maximization gaussian mixture approximate message passing"
    - "exploiting statistical dependencies in sparse representations for signal recovery"
    - "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 "sparse bayesian dictionary learning with a gaussian hierarchical model"):
  with members:
    - "cloud k svd a collaborative dictionary learning algorithm for big distributed data"
    - "simultaneous codeword optimization simco for dictionary update and learning"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing i motivation and construction"

Connected through hyperedge HE-6 (induced by source paper "super resolution compressed sensing a generalized iterative reweighted l2 approach"):
  with members:
    - "super resolution compressed sensing an iterative reweighted algorithm for joint parameter learning and sparse signal recovery"
    - "near minimax line spectral estimation"
    - "compressed sensing off the grid"
    - "towards a mathematical theory of super resolution"
    - "coherence pattern guided compressive sensing with unresolved grids"
    - "beyond nyquist efficient sampling of sparse bandlimited signals"

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)