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: "inference from correlated patterns a unified theory for perceptron learning and linear vector channels"
  Abstract: A framework to analyze inference performance in densely connected single-layer feed-forward networks is developed for situations where a given data set is composed of correlated patterns. The framework is based on the assumption that the left and right singular value bases of the given pattern matrix are generated independently and uniformly from Haar measures. This assumption makes it possible to characterize the objective system by a single function of two variables which is determined by the eigenvalue spectrum of the cross-correlation matrix of the pattern matrix. Links to existing methods for analysis of perceptron learning and Gaussian linear vector channels and an application to a simple but nontrivial problem are also shown.

Connected through hyperedge HE-1 (induced by source paper "macroscopic analysis of vector approximate message passing in a model mismatch setting"):
  with members:
    - "vector approximate message passing for the generalized linear model"
    - "turbo compressed sensing with partial dft sensing matrix"
    - "signal recovery using expectation consistent approximation for linear observations"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "optimal errors and phase transitions in high dimensional generalized linear models"):
  with members:
    - "rethinking generalization requires revisiting old ideas statistical mechanics approaches and complex learning behavior"
    - "information theoretic thresholds from the cavity method"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "the layered structure of tensor estimation and its mutual information"
    - "capacity achieving sparse superposition codes via approximate message passing decoding"
    - "opening the black box of deep neural networks via information"
    - "universal sparse superposition codes with spatial coupling and gamp decoding"
    - "structured signal recovery from quadratic measurements breaking sample complexity barriers via nonconvex optimization"

Connected through hyperedge HE-3 (induced by source paper "entropy and mutual information in models of deep neural networks"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "the stochastic interpolation method a simple scheme to prove replica formulas in bayesian inference"
    - "the layered structure of tensor estimation and its mutual information"
    - "multi layer generalized linear estimation"
    - "additivity of information in multilayer networks via additive gaussian noise transforms"
    - "opening the black box of deep neural networks via information"
    - "inference in deep networks in high dimensions"
    - "estimating mixture entropy with pairwise distances"

Connected through hyperedge HE-4 (induced by source paper "the mutual information in random linear estimation beyond i i d matrices"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "the stochastic interpolation method a simple scheme to prove replica formulas in bayesian inference"
    - "approximate message passing decoder and capacity achieving sparse superposition codes"
    - "the layered structure of tensor estimation and its mutual information"
    - "multi layer generalized linear estimation"
    - "additivity of information in multilayer networks via additive gaussian noise transforms"
    - "hybrid approximate message passing"
    - "phase transitions optimal errors and optimality of message passing in generalized linear models"

Connected through hyperedge HE-5 (induced by source paper "the committee machine computational to statistical gaps in learning a two layers neural network"):
  with members:
    - "overlap matrix concentration in optimal bayesian inference"
    - "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"

Connected through hyperedge HE-6 (induced by source paper "phase transitions optimal errors and optimality of message passing in generalized linear models"):
  with members:
    - "the layered structure of tensor estimation and its mutual information"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "proof of threshold saturation for spatially coupled sparse superposition codes"
    - "statistical physics and approximate message passing algorithms for sparse linear estimation problems in signal processing and coding theory"
    - "binary linear classification and feature selection via generalized approximate message passing"
    - "spectral redemption in clustering sparse networks"
    - "expectation maximization gaussian mixture approximate message passing"
    - "simultaneously structured models with application to sparse and low rank matrices"

Connected through hyperedge HE-7 (induced by source paper "multi layer generalized linear estimation"):
  with members:
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "the mutual information in random linear estimation"
    - "s amp approximate message passing for general matrix ensembles"
    - "signal recovery using expectation consistent approximation for linear observations"
    - "support recovery with sparsely sampled free random matrices"
    - "optimal phase transitions in compressed sensing"
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "generalized approximate message passing for estimation with random linear mixing"

Connected through hyperedge HE-8 (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"
    - "asymptotic mmse analysis under sparse representation modeling"
    - "statistical mechanics approach to sparse noise denoising"

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)