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: "task driven adaptive statistical compressive sensing of gaussian mixture models"
  Abstract: A framework for adaptive and non-adaptive statistical compressive sensing is developed, where a statistical model replaces the standard sparsity model of classical compressive sensing. We propose within this framework optimal task-specific sensing protocols specifically and jointly designed for classification and reconstruction. A two-step adaptive sensing paradigm is developed, where online sensing is applied to detect the signal class in the first step, followed by a reconstruction step adapted to the detected class and the observed samples. The approach is based on information theory, here tailored for Gaussian mixture models (GMMs), where an information-theoretic objective relationship between the sensed signals and a representation of the specific task of interest is maximized. Experimental results using synthetic signals, Landsat satellite attributes, and natural images of different sizes and with different noise levels show the improvements achieved using the proposed framework when compared to more standard sensing protocols. The underlying formulation can be applied beyond GMMs, at the price of higher mathematical and computational complexity.

Connected through hyperedge HE-1 (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"
    - "exact recovery conditions for sparse representations with partial support information"
    - "approximate message passing based compressed sensing reconstruction with generalized elastic net prior"
    - "communications inspired projection design with application to compressive sensing"

Connected through hyperedge HE-2 (induced by source paper "bounds on the number of measurements for reliable compressive classification"):
  with members:
    - "mismatch in the classification of linear subspaces sufficient conditions for reliable classification"
    - "reconstruction of signals drawn from a gaussian mixture via noisy compressive measurements"
    - "learning transformations for clustering and classification"
    - "sparse subspace clustering algorithm theory and applications"
    - "online adaptive statistical compressed sensing of gaussian mixture models"
    - "model based compressive sensing"

Connected through hyperedge HE-3 (induced by source paper "sequential information guided sensing"):
  with members:
    - "info greedy sequential adaptive compressed sensing"
    - "exact and stable covariance estimation from quadratic sampling via convex programming"
    - "near optimal adaptive compressed sensing"
    - "on the fundamental limits of recovering tree sparse vectors from noisy linear measurements"
    - "sketching sparse matrices"
    - "communications inspired projection design with application to compressive sensing"
    - "compressive binary search"
    - "sequential compressed sensing"

Connected through hyperedge HE-4 (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"
    - "communications inspired projection design with application to compressive sensing"
    - "statistical compressed sensing of gaussian mixture models"
    - "solving inverse problems with piecewise linear estimators from gaussian mixture models to structured sparsity"
    - "blind compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "reconstruction of signals drawn from a gaussian mixture via noisy compressive measurements"):
  with members:
    - "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"
    - "statistical compressed sensing of gaussian mixture models"

Connected through hyperedge HE-6 (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"
    - "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"
    - "compressive binary search"

Connected through hyperedge HE-7 (induced by source paper "adaptive rate compressive sensing using side information"):
  with members:
    - "the stone transform multi resolution image enhancement and real time compressive video"
    - "dynamic anomalography tracking network anomalies via sparsity and low rank"
    - "ls cs residual ls cs compressive sensing on least squares residual"
    - "sequential compressed sensing"
    - "modified cs modifying compressive sensing for problems with partially known support"
    - "kalman filtered compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "domain invariant face recognition using learned low rank transformation"):
  with members:
    - "communications inspired projection design with application to compressive sensing"
    - "robust principal component analysis"

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)