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: "the mutual information in random linear estimation beyond i i d matrices"
  Abstract: There has been definite progress recently in proving the variational single-letter formula given by the heuristic replica method for various estimation problems. In particular, the replica formula for the mutual information in the case of noisy linear estimation with random i.i.d. matrices, a problem with applications ranging from compressed sensing to statistics, has been proven rigorously. In this contribution we go beyond the restrictive i.i.d. matrix assumption and discuss the formula proposed by Takeda, Uda, Kabashima and later by Tulino, Verdu, Caire and Shamai who used the replica method. Using the recently introduced adaptive interpolation method and random matrix theory, we prove this formula for a relevant large sub-class of rotationally invariant matrices.

Connected through hyperedge HE-1 (induced by source paper "overlap matrix concentration in optimal bayesian inference"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "asymptotics of map inference in deep networks"
    - "entropy and mutual information in models of deep neural networks"
    - "mutual information for low rank even order symmetric tensor factorization"
    - "the committee machine computational to statistical gaps in learning a two layers neural network"
    - "rank one matrix estimation analysis of algorithmic and information theoretic limits by the spatial coupling method"
    - "adaptive path interpolation for sparse systems application to a simple censored block model"
    - "estimation in the spiked wigner model a short proof of the replica formula"

Connected through hyperedge HE-2 (induced by source paper "concentration of the matrix valued minimum mean square error in optimal bayesian inference"):
  with members:
    - "overlap matrix concentration in optimal bayesian inference"
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "asymptotics of map inference in deep networks"
    - "entropy and mutual information in models of deep neural networks"
    - "mutual information for low rank even order symmetric tensor factorization"
    - "the committee machine computational to statistical gaps in learning a two layers neural network"
    - "rank one matrix estimation analysis of algorithmic and information theoretic limits by the spatial coupling method"
    - "adaptive path interpolation for sparse systems application to a simple censored block model"

Connected through hyperedge HE-3 (induced by source paper "the stochastic interpolation method a simple scheme to prove replica formulas in bayesian inference"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "entropy and mutual information in models of deep neural networks"
    - "adaptive path interpolation for sparse systems application to a simple censored block model"
    - "information theoretic thresholds from the cavity method"
    - "the layered structure of tensor estimation and its mutual information"
    - "i mmse relations in random linear estimation and a sub extensive interpolation method"
    - "mutual information and optimality of approximate message passing in random linear estimation"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"

Connected through hyperedge HE-4 (induced by source paper "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"):
  with members:
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "expectation consistent approximate inference generalizations and convergence"
    - "the mutual information in random linear estimation"
    - "generalized turbo signal recovery for nonlinear measurements and orthogonal sensing matrices"
    - "performance improvement of iterative multiuser detection for large sparsely spread cdma systems by spatial coupling"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "s amp approximate message passing for general matrix ensembles"
    - "signal recovery using expectation consistent approximation for linear observations"

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)