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: "type i and type ii bayesian methods for sparse signal recovery using scale mixtures"
  Abstract: In this paper, we propose a generalized scale mixture family of distributions, namely the Power Exponential Scale Mixture (PESM) family, to model the sparsity inducing priors currently in use for sparse signal recovery (SSR). We show that the successful and popular methods such as LASSO, Reweighted    ${\ell }_{1}$   and Reweighted   ${\ell }_{2}$    methods can be formulated in an unified manner in a maximum a posteriori (MAP) or Type I Bayesian framework using an appropriate member of the PESM family as the sparsity inducing prior. In addition, exploiting the natural hierarchical framework induced by the PESM family, we utilize these priors in a Type II framework and develop the corresponding EM based estimation algorithms. Some insight into the differences between Type I and Type II methods is provided and of particular interest in the algorithmic development is the Type II variant of the popular and successful reweighted   ${\ell }_{1}$    method. Extensive empirical results are provided, and they show that the Type II methods exhibit better support recovery than the corresponding Type I methods.

Connected through hyperedge HE-1 (induced by source paper "a gamp based low complexity sparse bayesian learning algorithm"):
  with members:
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "fixed points of generalized approximate message passing with arbitrary matrices"
    - "efficient high dimensional inference in the multiple measurement vector problem"
    - "expectation maximization gaussian mixture approximate message passing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing i motivation and construction"

Connected through hyperedge HE-2 (induced by source paper "relevance subject machine a novel person re identification framework"):
  with members:
    - "robust bayesian method for simultaneous block sparse signal recovery with applications to face recognition"
    - "a comprehensive evaluation and benchmark for person re identification features metrics and datasets"
    - "deep recurrent convolutional networks for video based person re identification an end to end approach"
    - "top push video based person re identification"
    - "person re identification by local maximal occurrence representation and metric learning"
    - "robust face recognition via block sparse bayesian learning"
    - "sparsity and robustness in face recognition"

Connected through hyperedge HE-3 (induced by source paper "rectified gaussian scale mixtures and the sparse non negative least squares problem"):
  with members:
    - "a gamp based low complexity sparse bayesian learning algorithm"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "spatiotemporal sparse bayesian learning with applications to compressed sensing of multichannel physiological signals"
    - "an empirical bayes approach to recovering linearly constrained non negative sparse signals"
    - "robust face recognition via block sparse bayesian learning"
    - "expectation maximization gaussian mixture approximate message passing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing i motivation and construction"

Connected through hyperedge HE-4 (induced by source paper "robust bayesian method for simultaneous block sparse signal recovery with applications to face recognition"):
  with members:
    - "robust face recognition via block sparse bayesian learning"
    - "block sparse signals uncertainty relations and efficient recovery"

Connected through hyperedge HE-5 (induced by source paper "sparse bayesian inference of multivariable arx networks"):
  with members:
    - "linear dynamic network reconstruction from heterogeneous datasets"
    - "identifying biochemical reaction networks from heterogeneous datasets"

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.LG(Machine Learning)