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: "compressed sensing under matrix uncertainty optimum thresholds and robust approximate message passing"
  Abstract: In compressed sensing one measures sparse signals directly in a compressed form via a linear transform and then reconstructs the original signal. However, it is often the case that the linear transform itself is known only approximately, a situation called matrix uncertainty, and that the measurement process is noisy. Here we present two contributions to this problem: first, we use the replica method to determine the mean-squared error of the Bayes-optimal reconstruction of sparse signals under matrix uncertainty. Second, we consider a robust variant of the approximate message passing algorithm and demonstrate numerically that in the limit of large systems, this algorithm matches the optimal performance in a large region of parameters.

Connected through hyperedge HE-1 (induced by source paper "bilinear adaptive generalized vector approximate message passing"):
  with members:
    - "bilinear recovery using adaptive vector amp"
    - "plug in estimation in high dimensional linear inverse problems a rigorous analysis"
    - "hybrid approximate message passing"
    - "bayes optimal joint channel and data estimation for massive mimo with low precision adcs"
    - "parametric bilinear generalized approximate message passing"
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "vector approximate message passing for the generalized linear model"
    - "channel estimation in broadband millimeter wave mimo systems with few bit adcs"

Connected through hyperedge HE-2 (induced by source paper "parametric bilinear generalized approximate message passing"):
  with members:
    - "phase transitions and sample complexity in bayes optimal matrix factorization"
    - "self calibration and biconvex compressive sensing"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "compressive phase retrieval via generalized approximate message passing"
    - "identifiability in blind deconvolution under minimal assumptions"
    - "identifiability and stability in blind deconvolution under minimal assumptions"
    - "identifiability in blind deconvolution with subspace or sparsity constraints"
    - "fundamental limits of blind deconvolution part ii sparsity ambiguity trade offs"

Connected through hyperedge HE-3 (induced by source paper "performance limits of massive mimo systems based on bayes optimal inference"):
  with members:
    - "linear precoding for the mimo multiple access channel with finite alphabet inputs and statistical csi"
    - "massive mimo for next generation wireless systems"
    - "blind pilot decontamination"
    - "phase diagram and approximate message passing for blind calibration and dictionary learning"
    - "on an achievable rate of large rayleigh block fading mimo channels with no csi"
    - "a coordinated approach to channel estimation in large scale multiple antenna systems"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "statistical mechanical analysis of the kronecker channel model for multiple input multiple output wireless communication"

Connected through hyperedge HE-4 (induced by source paper "non adaptive pooling strategies for detection of rare faulty items"):
  with members:
    - "non adaptive group testing explicit bounds and novel algorithms"
    - "compressed sensing of approximately sparse signals phase transitions and optimal reconstruction"
    - "boolean compressed sensing and noisy group testing"
    - "compressed sensing with sparse structured matrices"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "threshold saturation via spatial coupling why convolutional ldpc ensembles perform so well over the bec"
    - "multiple access demodulation in the lifted signal graph with spatial coupling"
    - "improvement of bp based cdma multiuser detection by spatial coupling"

Connected through hyperedge HE-5 (induced by source paper "phase diagram and approximate message passing for blind calibration and dictionary learning"):
  with members:
    - "sample complexity of bayesian optimal dictionary learning"
    - "optimal phase transitions in compressed sensing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "on the local correctness of l 1 minimization for dictionary learning"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "message passing algorithms for compressed sensing i motivation and construction"
    - "message passing algorithms for compressed 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.IT(Information Theory)