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: "online adaptive statistical compressed sensing of gaussian mixture models"
  Abstract: A framework of online adaptive statistical compressed sensing is introduced for signals following a mixture model. The scheme first uses non-adaptive measurements, from which an online decoding scheme estimates the model selection. As soon as a candidate model has been selected, an optimal sensing scheme for the selected model continues to apply. The final signal reconstruction is calculated from the ensemble of both the non-adaptive and the adaptive measurements. For signals generated from a Gaussian mixture model, the online adaptive sensing algorithm is given and its performance is analyzed. On both synthetic and real image data, the proposed adaptive scheme considerably reduces the average reconstruction error with respect to standard statistical compressed sensing that uses fully random measurements, at a marginally increased computational complexity.

Connected through hyperedge HE-1 (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"
    - "task driven adaptive statistical compressive sensing of gaussian mixture models"
    - "sparse subspace clustering algorithm theory and applications"
    - "model based compressive sensing"

Connected through hyperedge HE-2 (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"
    - "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-3 (induced by source paper "compressive classification of a mixture of gaussians analysis designs and geometrical interpretation"):
  with members:
    - "reconstruction of signals drawn from a gaussian mixture via noisy compressive measurements"
    - "model based compressive sensing"

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)