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: "asymptotic mmse analysis under sparse representation modeling"
  Abstract: Compressed sensing is a signal processing technique in which data is acquired directly in a compressed form. There are two modeling approaches that can be considered: the worst-case (Hamming) approach and a statistical mechanism, in which the signals are modeled as random processes rather than as individual sequences. In this paper, the second approach is studied. In particular, we consider a model of the form $\boldsymbol{Y} = \boldsymbol{H}\boldsymbol{X}+\boldsymbol{W}$, where each comportment of $\boldsymbol{X}$ is given by $X_i = S_iU_i$, where $\left\{U_i\right\}$ are i.i.d. Gaussian random variables, and $\left\{S_i\right\}$ are binary random variables independent of $\left\{U_i\right\}$, and not necessarily independent and identically distributed (i.i.d.), $\boldsymbol{H}\in\mathbb{R}^{k\times n}$ is a random matrix with i.i.d. entries, and $\boldsymbol{W}$ is white Gaussian noise. Using a direct relationship between optimum estimation and certain partition functions, and by invoking methods from statistical mechanics and from random matrix theory (RMT), we derive an asymptotic formula for the minimum mean-square error (MMSE) of estimating the input vector $\boldsymbol{X}$ given $\boldsymbol{Y}$ and $\boldsymbol{H}$, as $k,n\to\infty$, keeping the measurement rate, $R = k/n$, fixed. In contrast to previous derivations, which are based on the replica method, the analysis carried out in this paper is rigorous.

Connected through hyperedge HE-1 (induced by source paper "understanding phase transitions via mutual information and mmse"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "multi layer generalized linear estimation"
    - "additivity of information in multilayer networks via additive gaussian noise transforms"
    - "generalized expectation consistent signal recovery for nonlinear measurements"
    - "dynamical functional theory for compressed sensing"
    - "phase transitions optimal errors and optimality of message passing in generalized linear models"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "expectation consistent approximate inference generalizations and convergence"

Connected through hyperedge HE-2 (induced by source paper "additivity of information in multilayer networks via additive gaussian noise transforms"):
  with members:
    - "multi layer generalized linear estimation"
    - "phase transitions optimal errors and optimality of message passing in generalized linear models"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "the mutual information in random linear estimation"
    - "vector approximate message passing for the generalized linear model"
    - "conditional central limit theorems for gaussian projections"
    - "s amp approximate message passing for general matrix ensembles"
    - "support recovery with sparsely sampled free random matrices"

Connected through hyperedge HE-3 (induced by source paper "statistical mechanics of map estimation general replica ansatz"):
  with members:
    - "glse precoders for massive mimo systems analysis and applications"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "asymptotics of nonlinear lse precoders with applications to transmit antenna selection"
    - "replica symmetry breaking in compressive sensing"
    - "universal sparse superposition codes with spatial coupling and gamp decoding"
    - "mutual information and optimality of approximate message passing in random linear estimation"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "threshold saturation of spatially coupled sparse superposition codes for all memoryless channels"

Connected through hyperedge HE-4 (induced by source paper "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"):
  with members:
    - "isotropically random orthogonal matrices performance of lasso and minimum conic singular values"
    - "on the performance of turbo signal recovery with partial dft sensing matrices"
    - "inference for generalized linear models via alternating directions and bethe free energy minimization"
    - "s amp approximate message passing for general matrix ensembles"
    - "signal recovery using expectation consistent approximation for linear observations"
    - "on sparse vector recovery performance in structurally orthogonal matrices via lasso"
    - "statistical mechanics approach to sparse noise denoising"
    - "support recovery with sparsely sampled free random matrices"

Connected through hyperedge HE-5 (induced by source paper "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"):
  with members:
    - "support recovery with sparsely sampled free random matrices"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "information theoretically optimal compressed sensing via spatial coupling and approximate message passing"
    - "optimum estimation via gradients of partition functions and information measures a statistical mechanical perspective"
    - "information theoretic proofs of entropy power inequalities"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "statistical physics of signal estimation in gaussian noise theory and examples of phase transitions"
    - "estimation in gaussian noise properties of the minimum mean square error"

Connected through hyperedge HE-6 (induced by source paper "signal recovery using expectation consistent approximation for linear observations"):
  with members:
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "approximate message passing with spatially coupled structured operators with applications to compressed sensing and sparse superposition codes"
    - "expectation propagation for approximate bayesian inference"
    - "support recovery with sparsely sampled free random matrices"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "a typical reconstruction limit of compressed sensing based on lp norm minimization"
    - "statistical mechanical analysis of the kronecker channel model for multiple input multiple output wireless communication"

Connected through hyperedge HE-7 (induced by source paper "universal decoding for gaussian intersymbol interference channels"):
  with members:
    - "analysis of mismatched estimation errors using gradients of partition functions"
    - "universal decoding for arbitrary channels relative to a given class of decoding metrics"
    - "communication over individual channels a general framework"
    - "the porosity of additive noise sequences"
    - "universal communication part i modulo additive channels"

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)