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: "bayesian compressive sensing via belief propagation"
  Abstract: Compressive sensing (CS) is an emerging field based on the revelation that a small collection of linear projections of a sparse signal contains enough information for stable, sub-Nyquist signal acquisition. When a statistical characterization of the signal is available, Bayesian inference can complement conventional CS methods based on linear programming or greedy algorithms. We perform approximate Bayesian inference using belief propagation (BP) decoding, which represents the CS encoding matrix as a graphical model. Fast computation is obtained by reducing the size of the graphical model with sparse encoding matrices. To decode a length-N signal containing K large coefficients, our CS-BP decoding algorithm uses O(Klog(N)) measurements and O(Nlog^2(N)) computation. Finally, although we focus on a two-state mixture Gaussian model, CS-BP is easily adapted to other signal models.

Connected through hyperedge HE-1 (induced by source paper "nonlinear function estimation with empirical bayes and approximate message passing"):
  with members:
    - "approximate message passing algorithm with universal denoising and gaussian mixture learning"
    - "empirical bayes and full bayes for signal estimation"
    - "recovery from linear measurements with complexity matching universal signal estimation"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "sparse volterra and polynomial regression models recoverability and estimation"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "graphical models concepts in compressed sensing"
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "fusion based cooperative support identification for compressive networked sensing"):
  with members:
    - "wireless compressive sensing over fading channels with distributed sparse random projections"
    - "compressed sensing for wireless communications useful tips and tricks"
    - "amplitude aided 1 bit compressive sensing over noisy wireless sensor networks"
    - "an improved rip based performance guarantee for sparse signal recovery via orthogonal matching pursuit"
    - "cooperative sparsity pattern recovery in distributed networks via distributed omp"
    - "collaborative spectrum sensing from sparse observations in cognitive radio networks"
    - "recovering compressively sampled signals using partial support information"

Connected through hyperedge HE-3 (induced by source paper "sat based compressive sensing"):
  with members:
    - "quantum annealing based binary compressive sensing with matrix uncertainty"

Connected through hyperedge HE-4 (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"
    - "statistical compressed sensing of gaussian mixture models"
    - "theoretical and empirical results for recovery from multiple measurements"
    - "iterative hard thresholding for compressed sensing"

Connected through hyperedge HE-5 (induced by source paper "statistical physics and information theory perspectives on linear inverse problems"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "approximate message passing algorithm with universal denoising and gaussian mixture learning"
    - "performance trade offs in multi processor approximate message passing"
    - "multi processor approximate message passing using lossy compression"
    - "phase transitions in sparse pca"
    - "compressive imaging via approximate message passing with image denoising"
    - "improving m sbl for joint sparse recovery using a subspace penalty"
    - "mismatched estimation in large linear systems"

Connected through hyperedge HE-6 (induced by source paper "performance limits for noisy multimeasurement vector problems"):
  with members:
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "the mutual information in random linear estimation"
    - "phase transitions in sparse pca"
    - "improving m sbl for joint sparse recovery using a subspace penalty"
    - "efficient high dimensional inference in the multiple measurement vector problem"
    - "performance regions in compressed sensing from noisy measurements"
    - "subspace methods for joint sparse recovery"

Connected through hyperedge HE-7 (induced by source paper "an active sequential xampling detector for spectrum sensing"):
  with members:
    - "utility maximizing sequential sensing over a finite horizon"
    - "converse bounds for noisy group testing with arbitrary measurement matrices"
    - "cross layer estimation and control for cognitive radio exploiting sparse network dynamics"
    - "info greedy sequential adaptive compressed sensing"
    - "sub nyquist sampling for power spectrum sensing in cognitive radios a unified approach"
    - "wideband spectrum sensing with sub nyquist sampling in cognitive radios"
    - "boolean compressed sensing and noisy group testing"
    - "on optimality of myopic policy for restless multi armed bandit problem an axiomatic approach"

Connected through hyperedge HE-8 (induced by source paper "deep learning sparse ternary projections for compressed sensing of images"):
  with members:
    - "binarized neural networks training deep neural networks with weights and activations constrained to 1 or 1"
    - "a deep learning approach to block based compressed sensing of images"
    - "xnor net imagenet classification using binary convolutional neural networks"
    - "binaryconnect training deep neural networks with binary weights during propagations"
    - "deep compression compressing deep neural networks with pruning trained quantization and huffman coding"
    - "learning both weights and connections for efficient neural networks"
    - "a deep learning approach to structured signal recovery"
    - "batch normalization accelerating deep network training by reducing internal covariate shift"

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)