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: "statistical mechanics of map estimation general replica ansatz"
  Abstract: The large-system performance of MAP estimation is studied considering a general distortion function when the observation vector is received through a linear system with additive white Gaussian noise. The analysis considers the system matrix to be chosen from the large class of rotationally invariant random matrices. We take a statistical mechanical approach by introducing a spin glass corresponding to the estimator, and employing the replica method for the large-system analysis. In contrast to earlier replica based studies, our analysis evaluates the general replica ansatz of the corresponding spin glass and determines the asymptotic distortion of the estimator for any structure of the replica correlation matrix. Consequently, the replica symmetric as well as the Replica Symmetry (RS) breaking ansatz with $b$ steps of breaking is deduced from the given general replica ansatz. The generality of our distortion function lets us derive a more general form of the MAP decoupling principle. Based on the general replica ansatz, we show that for any structure of the replica correlation matrix, the vector-valued system decouples into a bank of equivalent decoupled linear systems followed by MAP estimators. The structure of the decoupled linear system is further studied under both the RS and the Replica Symmetry Breaking (RSB) assumptions. For $b$ steps of RSB, the decoupled system is found to be an additive system with a noise term given as the sum of an independent Gaussian random variable with $b$ correlated impairment terms. As an application of our study, we investigate large compressive sensing systems by considering the $\ell_p$ minimization recovery schemes. Our numerical investigations show that the replica symmetric ansatz for $\ell_0$ norm recovery fails to give an accurate approximation of the mean square error as the compression rate grows, and therefore, the RSB ans\"atze are needed.

Connected through hyperedge HE-1 (induced by source paper "rls based detection for massive spatial modulation mimo"):
  with members:
    - "maximum a posteriori signal recovery with prior information applications to compressive sensing"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"

Connected through hyperedge HE-2 (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-3 (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"
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "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-4 (induced by source paper "cnn based signal detection for banded linear systems"):
  with members:
    - "optimal data detection in large mimo"
    - "power of deep learning for channel estimation and signal detection in ofdm systems"
    - "learning to decode linear codes using deep learning"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "deep residual learning for image recognition"
    - "scalable uplink signal detection in c rans via randomized gaussian message passing"
    - "convolutional neural networks for sentence classification"
    - "generalized approximate message passing for estimation with random linear mixing"

Connected through hyperedge HE-5 (induced by source paper "theoretical bounds on map estimation in distributed sensing networks"):
  with members:
    - "performance limits for noisy multimeasurement vector problems"
    - "performance analysis of approximate message passing for distributed compressed sensing"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "rsb decoupling property of map estimators"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"

Connected through hyperedge HE-6 (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"
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "asymptotic analysis of map estimation via the replica method and applications to compressed sensing"

Connected through hyperedge HE-7 (induced by source paper "maximum a posteriori signal recovery with prior information applications to compressive sensing"):
  with members:
    - "replica symmetry breaking in compressive sensing"
    - "applications of large random matrices in communications engineering"
    - "support driven reweighted ell_1 minimization"

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"
    - "analysis of regularized ls reconstruction and random matrix ensembles in compressed sensing"
    - "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)