human: Given a hyperedge-centered hypergraph: <hypergraph>, where the center hyperedge is induced by one source paper and its member nodes are the cited papers.

Details of the hypergraph:
Center hyperedge (induced by source paper "learning and free energy in expectation consistent approximate inference"):
  Abstract: Approximations of loopy belief propagation are commonly combined with expectation-maximization (EM) for probabilistic inference problems when the densities have unknown parameters. This work considers an approximate EM learning method combined with Opper and Winther's Expectation Consistent Approximate Inference method. The combined algorithm is called EM-EC and is shown to have a simple variational free energy interpretation. In addition, the algorithm can provide a computationally efficient and general approach to a number of learning problems with hidden states including empirical Bayesian forms of regression, classification, compressed sensing, and sparse Bayesian learning. Systems with linear dynamics interconnected with non-Gaussian or nonlinear components can also be easily considered.

Includes members:
  Member "expectation consistent approximate inference generalizations and convergence":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "inference in multi layer networks with matrix valued unknowns")
  Member "inference for generalized linear models via alternating directions and bethe free energy minimization":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "an approximate message passing framework for side information")
  Member "variational free energies for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "inference with deep generative priors in high dimensions")
  Member "loopy belief propagation for approximate inference an empirical study":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "analyzing differentiable fuzzy logic operators")
  Member "expectation maximization gaussian mixture approximate message passing":
    also connected through other hyperedges:
      - Hyperedge HE-5 (induced by source paper "massive unsourced random access based on uncoupled compressive sensing another blessing of massive mimo")
  Member "approximate message passing with consistent parameter estimation and applications to sparse learning":
    also connected through other hyperedges:
      - Hyperedge HE-6 (induced by source paper "bilinear recovery using adaptive vector amp")

Question: Please tell me which class the source paper 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)