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 "the linearization of pairwise markov networks"):
  Abstract: Belief Propagation (BP) allows to approximate exact probabilistic inference in graphical models, such as Markov networks (also called Markov random fields, or undirected graphical models). However, no exact convergence guarantees for BP are known, in general. Recent work has proposed to approximate BP by linearizing the update equations around default values for the special case when all edges in the Markov network carry the same symmetric, doubly stochastic potential. This linearization has led to exact convergence guarantees, considerable speed-up, while maintaining high quality results in network-based classification (i.e. when we only care about the most likely label or class for each node and not the exact probabilities). The present paper generalizes our prior work on Linearized Belief Propagation (LinBP) with an approach that approximates Loopy Belief Propagation on any pairwise Markov network with the problem of solving a linear equation system.

Includes members:
  Member "linearized and single pass belief propagation":
    also connected through other hyperedges:
      - Hyperedge HE-1 (induced by source paper "factorized graph representations for semi supervised learning from sparse data")
  Member "residual belief propagation informed scheduling for asynchronous message passing":
    also connected through other hyperedges:
      - Hyperedge HE-2 (induced by source paper "nonlinear equation solving a faster alternative to feedforward computation")
  Member "message passing algorithms for compressed sensing":
    also connected through other hyperedges:
      - Hyperedge HE-3 (induced by source paper "capacity achieving spatially coupled sparse superposition codes with amp decoding")
  Member "sufficient conditions for convergence of the sum product algorithm":
    also connected through other hyperedges:
      - Hyperedge HE-4 (induced by source paper "factorized graph representations for semi supervised learning from sparse data")

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.AI(Artificial Intelligence)