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: "expectation maximization as message passing part i principles and gaussian messages"
  Abstract: It is shown how expectation maximization (EM) may be viewed as a message passing algorithm in factor graphs. In particular, a general EM message computation rule is identified. As a factor graph tool, EM may be used to break cycles in a factor graph, and tractable messages may in some cases be obtained where the sum-product messages are unwieldy. #R##N#As an exemplary application, the paper considers linear Gaussian state space models. Unknown coefficients in such models give rise to multipliers in the corresponding factor graph. A main attraction of EM in such cases is that it results in purely Gaussian message passing algorithms. These Gaussian EM messages are tabulated for several (scalar, vector, matrix) multipliers that frequently appear in applications.

Connected through hyperedge HE-1 (induced by source paper "on sparsity by nuv em gaussian message passing and kalman smoothing"):
  with members:
    - "message passing algorithms for compressed sensing"

Connected through hyperedge HE-2 (induced by source paper "joint channel estimation and channel decoding in physical layer network coding systems an em bp factor graph framework"):
  with members:
    - "asynchronous physical layer network coding"
    - "physical layer network coding tutorial survey and beyond"
    - "a message passing approach for joint channel estimation interference mitigation and decoding"
    - "channel coding and decoding in a relay system operated with physical layer network coding"
    - "expectation maximization as message passing"

Connected through hyperedge HE-3 (induced by source paper "entropy message passing"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "expectation maximization as message passing"

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)