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: "statistical compressed sensing of gaussian mixture models"
  Abstract: A novel framework of compressed sensing, namely statistical compressed sensing (SCS), that aims at efficiently sampling a collection of signals that follow a statistical distribution, and achieving accurate reconstruction on average, is introduced. SCS based on Gaussian models is investigated in depth. For signals that follow a single Gaussian model, with Gaussian or Bernoulli sensing matrices of O(k) measurements, considerably smaller than the O(k log(N/k)) required by conventional CS based on sparse models, where N is the signal dimension, and with an optimal decoder implemented via linear filtering, significantly faster than the pursuit decoders applied in conventional CS, the error of SCS is shown tightly upper bounded by a constant times the best k-term approximation error, with overwhelming probability. The failure probability is also significantly smaller than that of conventional sparsity-oriented CS. Stronger yet simpler results further show that for any sensing matrix, the error of Gaussian SCS is upper bounded by a constant times the best k-term approximation with probability one, and the bound constant can be efficiently calculated. For Gaussian mixture models (GMMs), that assume multiple Gaussian distributions and that each signal follows one of them with an unknown index, a piecewise linear estimator is introduced to decode SCS. The accuracy of model selection, at the heart of the piecewise linear decoder, is analyzed in terms of the properties of the Gaussian distributions and the number of sensing measurements. A maximization-maximization (Max-Max) algorithm that iteratively estimates the Gaussian models parameters, the signals model selection, and decodes the signals, is presented for GMM-based SCS. In real image sensing applications, GMM-based SCS is shown to lead to improved results compared to conventional CS, at a considerably lower computational cost.

Connected through hyperedge HE-1 (induced by source paper "compressed anomaly detection with multiple mixed observations"):
  with members:
    - "distributed compressed sensing for static and time varying networks"
    - "compressed hypothesis testing to mix or not to mix"
    - "measurement bounds for sparse signal ensembles via graphical models"
    - "on the limits of sequential testing in high dimensions"
    - "theoretical and empirical results for recovery from multiple measurements"
    - "iterative hard thresholding for compressed sensing"
    - "bayesian compressive sensing via belief propagation"

Connected through hyperedge HE-2 (induced by source paper "coherence pursuit fast simple and robust principal component analysis"):
  with members:
    - "fast robust and non convex subspace recovery"
    - "high dimensional low rank plus sparse matrix decomposition"
    - "innovation pursuit a new approach to subspace clustering"
    - "fast algorithms for robust pca via gradient descent"
    - "identifying outliers in large matrices via randomized adaptive compressive sampling"
    - "a subspace learning approach to high dimensional matrix decomposition with efficient information sampling"
    - "dual principal component pursuit"
    - "robust computation of linear models by convex relaxation"

Connected through hyperedge HE-3 (induced by source paper "classification and reconstruction of high dimensional signals from low dimensional features in the presence of side information"):
  with members:
    - "reconstruction of signals drawn from a gaussian mixture via noisy compressive measurements"
    - "multi terminal probabilistic compressed sensing"
    - "compressed sensing with prior information optimal strategies geometry and bounds"
    - "compressive classification of a mixture of gaussians analysis designs and geometrical interpretation"
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "task driven adaptive statistical compressive sensing of gaussian mixture models"
    - "exact recovery conditions for sparse representations with partial support information"
    - "approximate message passing based compressed sensing reconstruction with generalized elastic net prior"

Connected through hyperedge HE-4 (induced by source paper "identifying outliers in large matrices via randomized adaptive compressive sampling"):
  with members:
    - "robust computation of linear models by convex relaxation"
    - "on the fundamental limits of recovering tree sparse vectors from noisy linear measurements"
    - "sparse signal processing with linear and non linear observations a unified shannon theoretic approach"
    - "compressive principal component pursuit"
    - "lower bounds for adaptive sparse recovery"
    - "compressive binary search"
    - "sketched svd recovering spectral features from compressive measurements"
    - "algorithms and hardness for robust subspace recovery"

Connected through hyperedge HE-5 (induced by source paper "compressive hyperspectral imaging with side information"):
  with members:
    - "classification and reconstruction of high dimensional signals from low dimensional features in the presence of side information"
    - "task driven adaptive statistical compressive sensing of gaussian mixture models"
    - "communications inspired projection design with application to compressive sensing"
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"
    - "blind compressed sensing"

Connected through hyperedge HE-6 (induced by source paper "reconstruction of signals drawn from a gaussian mixture via noisy compressive measurements"):
  with members:
    - "task driven adaptive statistical compressive sensing of gaussian mixture models"
    - "communications inspired projection design with application to compressive sensing"
    - "support recovery with sparsely sampled free random matrices"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "communications inspired linear discriminant analysis"
    - "online adaptive statistical compressed sensing of gaussian mixture models"
    - "optimal phase transitions in compressed sensing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "info greedy sequential adaptive compressed sensing"):
  with members:
    - "reconstruction of signals drawn from a gaussian mixture via noisy compressive measurements"
    - "information theoretic bounds for adaptive sparse recovery"
    - "near optimal adaptive compressed sensing"
    - "task driven adaptive statistical compressive sensing of gaussian mixture models"
    - "distance based and continuum fano inequalities with applications to statistical estimation"
    - "on the fundamental limits of recovering tree sparse vectors from noisy linear measurements"
    - "performance guarantees for adaptive estimation of sparse signals"
    - "communications inspired projection design with application to compressive sensing"

Connected through hyperedge HE-8 (induced by source paper "fundamental performance limits for ideal decoders in high dimensional linear inverse problems"):
  with members:
    - "breaking the coherence barrier a new theory for compressed sensing"
    - "new analysis of manifold embeddings and signal recovery from compressive measurements"
    - "simultaneously structured models with application to sparse and low rank matrices"
    - "signal space cosamp for sparse recovery with redundant dictionaries"
    - "stable image reconstruction using total variation minimization"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"
    - "a probabilistic and ripless theory of compressed sensing"
    - "block sparse signals uncertainty relations and efficient recovery"

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)