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: "maximin analysis of message passing algorithms for recovering block sparse signals"
  Abstract: We consider the problem of recovering a block (or group) sparse signal from an underdetermined set of random linear measurements, which appear in compressed sensing applications such as radar and imaging. Recent results of Donoho, Johnstone, and Montanari have shown that approximate message passing (AMP) in combination with Stein's shrinkage outperforms group LASSO for large block sizes. In this paper, we prove that, for a fixed block size and in the strong undersampling regime (i.e., having very few measurements compared to the ambient dimension), AMP cannot improve upon group LASSO, thereby complementing the results of Donoho et al.

Connected through hyperedge HE-1 (induced by source paper "plug in estimation in high dimensional linear inverse problems a rigorous analysis"):
  with members:
    - "hybrid approximate message passing"
    - "beyond a gaussian denoiser residual learning of deep cnn for image denoising"
    - "state evolution for approximate message passing with non separable functions"
    - "expectation consistent approximate inference generalizations and convergence"
    - "parametric bilinear generalized approximate message passing"
    - "self calibration and biconvex compressive sensing"
    - "adaptive damping and mean removal for the generalized approximate message passing algorithm"
    - "hybrid approximate message passing"

Connected through hyperedge HE-2 (induced by source paper "performance analysis of approximate message passing for distributed compressed sensing"):
  with members:
    - "analysis of approximate message passing with a class of non separable denoisers"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "performance limits for noisy multimeasurement vector problems"
    - "hybrid approximate message passing"
    - "state evolution for approximate message passing with non separable functions"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "omp based joint sparsity pattern recovery under communication constraints"
    - "efficient high dimensional inference in the multiple measurement vector problem"

Connected through hyperedge HE-3 (induced by source paper "precise error analysis of regularized m estimators in high dimensions"):
  with members:
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "ber analysis of the box relaxation for bpsk signal recovery"
    - "convex recovery of a structured signal from independent random linear measurements"
    - "isotropically random orthogonal matrices performance of lasso and minimum conic singular values"
    - "the generalized lasso with non linear observations"
    - "corrupted sensing novel guarantees for separating structured signals"
    - "statistical estimation from denoising to sparse regression and hidden cliques"
    - "convexity in source separation models geometry and algorithms"

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)