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 recovery from a bayesian point of view the variational approach"
  Abstract: In this paper, we consider the phase recovery problem, where a complex signal vector has to be estimated from the knowledge of the modulus of its linear projections, from a naive variational Bayesian point of view. In particular, we derive an iterative algorithm following the minimization of the Kullback-Leibler divergence under the mean-field assumption, and show on synthetic data with random projections that this approach leads to an efficient and robust procedure, with a reasonable computational cost.

Connected through hyperedge HE-1 (induced by source paper "an expectation maximization approach to tuning generalized vector approximate message passing"):
  with members:
    - "hybrid approximate message passing"
    - "expectation consistent approximate inference generalizations and convergence"
    - "learning and free energies for vector approximate message passing"
    - "vector approximate message passing for the generalized linear model"
    - "compressive phase retrieval via generalized approximate message passing"
    - "expectation maximization gaussian mixture approximate message passing"
    - "hybrid approximate message passing"

Connected through hyperedge HE-2 (induced by source paper "phase retrieval with a multivariate von mises prior from a bayesian formulation to a lifting solution"):
  with members:
    - "compressive phase retrieval via generalized approximate message passing"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

Connected through hyperedge HE-3 (induced by source paper "doa estimation in structured phase noisy environments technical report"):
  with members:
    - "compressive phase retrieval via generalized approximate message passing"
    - "phaselift exact and stable signal recovery from magnitude measurements via convex programming"

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)