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: "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
  Abstract: The performance of regularized least-squares estimation in noisy compressed sensing is analyzed in the limit when the dimensions of the measurement matrix grow large. The sensing matrix is consider ...

Connected through hyperedge HE-1 (induced by source paper "ssfn self size estimating feed forward network with low complexity limited need for human intervention and consistent behaviour across trials"):
  with members:
    - "do cifar 10 classifiers generalize to cifar 10"
    - "autoaugment learning augmentation policies from data"
    - "layer wise training of deep networks using kernel similarity"
    - "progressive learning for systematic design of large neural networks"
    - "neural architecture search with reinforcement learning"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-2 (induced by source paper "spectral method for phase retrieval an expectation propagation perspective"):
  with members:
    - "optimal spectral initialization for signal recovery with applications to phase retrieval"
    - "expectation propagation for approximate inference free probability framework"
    - "optimization based amp for phase retrieval the impact of initialization and ell_2 regularization"
    - "rigorous dynamics of expectation propagation based signal recovery from unitarily invariant measurements"
    - "generalized expectation consistent signal recovery for nonlinear measurements"
    - "expectation consistent approximate inference generalizations and convergence"
    - "vector approximate message passing for the generalized linear model"
    - "generalized turbo signal recovery for nonlinear measurements and orthogonal sensing matrices"

Connected through hyperedge HE-3 (induced by source paper "ssfn self size estimating feed forward network and low complexity design"):
  with members:
    - "neural architecture search with reinforcement learning"
    - "deep residual learning for image recognition"
    - "adam a method for stochastic optimization"
    - "fastfood approximate kernel expansions in loglinear time"
    - "representation learning a review and new perspectives"

Connected through hyperedge HE-4 (induced by source paper "glse precoders for massive mimo systems analysis and applications"):
  with members:
    - "stepwise transmit antenna selection in downlink massive multiuser mimo"
    - "nonlinear precoders for massive mimo systems with general constraints"
    - "the stochastic interpolation method a simple scheme to prove replica formulas in bayesian inference"
    - "asymptotics of nonlinear lse precoders with applications to transmit antenna selection"
    - "asymptotics of transmit antenna selection impact of multiple receive antennas"
    - "energy efficient coordinated multi cell multi group multicast beamforming with antenna selection"
    - "a new class of nonlinear precoders for hardware efficient massive mimo systems"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"

Connected through hyperedge HE-5 (induced by source paper "rls recovery with asymmetric penalty fundamental limits and algorithmic approaches"):
  with members:
    - "precoding via approximate message passing with instantaneous signal constraints"
    - "maximum a posteriori signal recovery with prior information applications to compressive sensing"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "rsb decoupling property of map estimators"
    - "statistical mechanics of map estimation general replica ansatz"
    - "support recovery with sparsely sampled free random matrices"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "support driven reweighted ell_1 minimization"

Connected through hyperedge HE-6 (induced by source paper "matrix infinitely divisible series tail inequalities and applications in optimization"):
  with members:
    - "recycling randomness with structure for sublinear time kernel expansions"
    - "an exploration of parameter redundancy in deep networks with circulant projections"
    - "from random matrix theory to coding theory volume of a metric ball in unitary group"
    - "robust estimates of covariance matrices in the large dimensional regime"
    - "improved bounds for the nystrom method with application to kernel classification"
    - "on the theorem of uniform recovery of random sampling matrices"

Connected through hyperedge HE-7 (induced by source paper "oversampled adaptive sensing with random projections analysis and algorithmic approaches"):
  with members:
    - "maximum a posteriori signal recovery with prior information applications to compressive sensing"
    - "statistical mechanics of map estimation general replica ansatz"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"

Connected through hyperedge HE-8 (induced by source paper "replica symmetry breaking in compressive sensing"):
  with members:
    - "rsb decoupling property of map estimators"
    - "lse precoders for massive mimo with hardware constraints fundamental limits"
    - "statistical mechanics of map estimation general replica ansatz"
    - "support recovery with sparsely sampled free random matrices"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"
    - "optimal phase transitions in compressed sensing"
    - "a typical reconstruction limit of compressed sensing based on lp norm minimization"
    - "vector precoding for wireless mimo systems and its replica analysis"

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)