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: "convergent propagation algorithms via oriented trees"
  Abstract: Inference problems in graphical models are often approximated by casting them as constrained optimization problems. Message passing algorithms, such as belief propagation, have previously been suggested as methods for solving these optimization problems. However, there are few convergence guarantees for such algorithms, and the algorithms are therefore not guaranteed to solve the corresponding optimization problem. Here we present an oriented tree decomposition algorithm that is guaranteed to converge to the global optimum of the Tree-Reweighted (TRW) variational problem. Our algorithm performs local updates in the convex dual of the TRW problem - an unconstrained generalized geometric program. Primal updates, also local, correspond to oriented reparametrization operations that leave the distribution intact.

Connected through hyperedge HE-1 (induced by source paper "lifted tree reweighted variational inference"):
  with members:
    - "on the complexity and approximation of binary evidence in lifted inference"
    - "automorphism groups of graphical models and lifted variational inference"
    - "template based inference in symmetric relational markov random fields"
    - "tightening lp relaxations for map using message passing"
    - "markov chains on orbits of permutation groups"
    - "counting belief propagation"
    - "a new class of upper bounds on the log partition function"
    - "probabilistic theorem proving"

Connected through hyperedge HE-2 (induced by source paper "generalized belief propagation on tree robust structured region graphs"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "belief optimization for binary networks a stable alternative to loopy belief propagation"
    - "sufficient conditions for convergence of loopy belief propagation"
    - "on the choice of regions for generalized belief propagation"
    - "convergent message passing algorithms for inference over general graphs with convex free energies"
    - "iterative join graph propagation"
    - "residual belief propagation informed scheduling for asynchronous message passing"
    - "structured region graphs morphing ep into gbp"

Connected through hyperedge HE-3 (induced by source paper "convexifying the bethe free energy"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "belief optimization for binary networks a stable alternative to loopy belief propagation"
    - "on the choice of regions for generalized belief propagation"
    - "convergent message passing algorithms for inference over general graphs with convex free energies"
    - "map estimation linear programming and belief propagation with convex free energies"
    - "a new class of upper bounds on the log partition function"

Connected through hyperedge HE-4 (induced by source paper "tightening fractional covering upper bounds on the partition function for high order region graphs"):
  with members:
    - "message passing algorithms reparameterizations and splittings"
    - "convergent message passing algorithms for inference over general graphs with convex free energies"
    - "convergent message passing algorithms a unifying view"
    - "tightening lp relaxations for map using message passing"
    - "a new class of upper bounds on the log partition function"
    - "information inequalities for joint distributions with interpretations and applications"

Connected through hyperedge HE-5 (induced by source paper "convergent message passing algorithms for inference over general graphs with convex free energies"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "sufficient conditions for convergence of loopy belief propagation"
    - "map estimation linear programming and belief propagation with convex free energies"
    - "a new class of upper bounds on the log partition function"

Connected through hyperedge HE-6 (induced by source paper "efficient mrf energy minimization via adaptive diminishing smoothing"):
  with members:
    - "convergent message passing algorithms a unifying view"
    - "a new class of upper bounds on the log partition function"
    - "lagrangian relaxation for map estimation in graphical models"

Connected through hyperedge HE-7 (induced by source paper "convergent message passing algorithms a unifying view"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "a new class of upper bounds on the log partition function"
    - "norm product belief propagation primal dual message passing for approximate inference"

Connected through hyperedge HE-8 (induced by source paper "norm product belief propagation primal dual message passing for approximate inference"):
  with members:
    - "sufficient conditions for convergence of loopy belief propagation"
    - "convergent message passing algorithms for inference over general graphs with convex free energies"
    - "map estimation linear programming and belief propagation with convex free energies"
    - "convergent message passing algorithms a unifying view"
    - "tightening lp relaxations for map using message passing"
    - "on the optimality of tree reweighted max product message passing"
    - "convexifying the bethe free energy"
    - "a new class of upper bounds on the log partition function"

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.LG(Machine Learning)