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: "overlap matrix concentration in optimal bayesian inference"
  Abstract: We consider models of Bayesian inference of signals with vectorial components of finite dimensionality. We show that, under a proper perturbation, these models are replica symmetric in the sense that the overlap matrix concentrates. The overlap matrix is the order parameter in these models and is directly related to error metrics such as minimum mean-square errors. Our proof is valid in the optimal Bayesian inference setting. This means that it relies on the assumption that the model and all its hyper-parameters are known so that the posterior distribution can be written exactly. Examples of important problems in high-dimensional inference and learning to which our results apply are low-rank tensor factorization, the committee machine neural network with a finite number of hidden neurons in the teacher-student scenario, or multi-layer versions of the generalized linear model.

Connected through hyperedge HE-1 (induced by source paper "concentration of the matrix valued minimum mean square error 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"
    - "the mutual information in random linear estimation beyond i i d matrices"
    - "adaptive path interpolation for sparse systems application to a simple censored block model"

Connected through hyperedge HE-2 (induced by source paper "mutual information for low rank even order symmetric tensor factorization"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "estimation in the spiked wigner model a short proof of the replica formula"
    - "the layered structure of tensor estimation and its mutual information"
    - "mutual information in rank one matrix estimation"
    - "mutual information for symmetric rank one matrix estimation a proof of the replica formula"
    - "asymptotic mutual information for the two groups stochastic block model"
    - "a statistical model for tensor pca"

Connected through hyperedge HE-3 (induced by source paper "the committee machine computational to statistical gaps in learning a two layers neural network"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "mutual information for low rank even order symmetric tensor factorization"
    - "rethinking generalization requires revisiting old ideas statistical mechanics approaches and complex learning behavior"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "decoding from pooled data phase transitions of message passing"
    - "performance limits for noisy multimeasurement vector problems"
    - "spurious local minima are common in two layer relu neural networks"
    - "mutual information and optimality of approximate message passing in random linear estimation"

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)