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: "phase transitions optimal errors and optimality of message passing in generalized linear models"
  Abstract: We consider generalized linear models where an unknown $n$-dimensional signal vector is observed through the successive application of a random matrix and a non-linear (possibly probabilistic) componentwise function. We consider the models in the high-dimensional limit, where the observation consists of $m$ points, and $m/n {\to} {\alpha}$ where ${\alpha}$ stays finite in the limit $m, n {\to} {\infty}$. This situation is ubiquitous in applications ranging from supervised machine learning to signal processing. A substantial amount of work suggests that both the inference and learning tasks in these problems have sharp intrinsic limitations when the available data become too scarce or too noisy. Here, we provide rigorous asymptotic predictions for these thresholds through the proof of a simple expression for the mutual information between the observations and the signal. Thanks to this expression we also obtain as a consequence the optimal value of the generalization error in many statistical learning models of interest, such as the teacher-student binary perceptron, and introduce several new models with remarquable properties. We compute these thresholds (or "phase transitions") using ideas from statistical physics that are turned into rigorous methods thanks to a new powerful smart-path interpolation technique called the stochastic interpolation method, which has recently been introduced by two of the authors. Moreover we show that a polynomial-time algorithm refered to as generalized approximate message-passing reaches the optimal generalization performance for a large set of parameters in these problems. Our results clarify the difficulties and challenges one has to face when solving complex high-dimensional statistical problems.

Connected through hyperedge HE-1 (induced by source paper "understanding phase transitions via mutual information and mmse"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "multi layer generalized linear estimation"
    - "additivity of information in multilayer networks via additive gaussian noise transforms"
    - "generalized expectation consistent signal recovery for nonlinear measurements"
    - "dynamical functional theory for compressed sensing"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "expectation consistent approximate inference generalizations and convergence"
    - "a survey of stochastic simulation and optimization methods in signal processing"

Connected through hyperedge HE-2 (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"
    - "mutual information and optimality of approximate message passing in random linear estimation"

Connected through hyperedge HE-3 (induced by source paper "estimation in the spiked wigner model a short proof of the replica formula"):
  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"
    - "mutual information in rank one matrix estimation"
    - "mutual information for symmetric rank one matrix estimation a proof of the replica formula"
    - "phase transitions in sparse pca"
    - "a statistical model for tensor pca"

Connected through hyperedge HE-4 (induced by source paper "additivity of information in multilayer networks via additive gaussian noise transforms"):
  with members:
    - "multi layer generalized linear estimation"
    - "the replica symmetric prediction for compressed sensing with gaussian matrices is exact"
    - "the mutual information in random linear estimation"
    - "vector approximate message passing for the generalized linear model"
    - "conditional central limit theorems for gaussian projections"
    - "s amp approximate message passing for general matrix ensembles"
    - "asymptotic mmse analysis under sparse representation modeling"
    - "support recovery with sparsely sampled free random matrices"

Connected through hyperedge HE-5 (induced by source paper "the layered structure of tensor estimation and its mutual information"):
  with members:
    - "optimal errors and phase transitions in high dimensional generalized linear models"
    - "mutual information and optimality of approximate message passing in random linear estimation"
    - "the mutual information in random linear estimation"
    - "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"
    - "mmse of probabilistic low rank matrix estimation universality with respect to the output channel"
    - "tensor decompositions for signal processing applications from two way to multiway component 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)