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"
  Abstract: Based on prior work by Eckford, it is shown how expectation maximization (EM) may be viewed, and used, as a message passing algorithm in factor graphs

Connected through hyperedge HE-1 (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 part i principles and gaussian messages"

Connected through hyperedge HE-2 (induced by source paper "a message passing receiver for bicm ofdm over unknown clustered sparse channels"):
  with members:
    - "the dynamics of message passing on dense graphs with applications to compressed sensing"
    - "on communication over unknown sparse frequency selective block fading channels"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "belief propagation based joint channel estimation and decoding for spectrally efficient communication over unknown sparse channels"
    - "estimation with random linear mixing belief propagation and compressed sensing"
    - "message passing algorithms for compressed sensing"
    - "bayesian compressive sensing via belief propagation"

Connected through hyperedge HE-3 (induced by source paper "merging belief propagation and the mean field approximation a free energy approach"):
  with members:
    - "message passing algorithms for channel estimation and decoding using approximate inference"
    - "a new class of upper bounds on the log partition function"
    - "distributed iterative processing for interference channels with receiver cooperation"

Connected through hyperedge HE-4 (induced by source paper "entropy message passing"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "expectation maximization as message passing part i principles and gaussian messages"

Connected through hyperedge HE-5 (induced by source paper "belief propagation based joint channel estimation and decoding for spectrally efficient communication over unknown sparse channels"):
  with members:
    - "a message passing receiver for bicm ofdm over unknown clustered sparse channels"
    - "on communication over unknown sparse frequency selective block fading channels"
    - "generalized approximate message passing for estimation with random linear mixing"
    - "estimation with random linear mixing belief propagation and compressed sensing"
    - "a message passing approach for joint channel estimation interference mitigation and decoding"
    - "message passing algorithms for compressed sensing"
    - "bayesian compressive sensing via belief propagation"

Connected through hyperedge HE-6 (induced by source paper "expectation maximization as message passing part i principles and gaussian messages"):
  with members:
    - "a generalized mean field algorithm for variational inference in exponential families"

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)