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 message passing algorithms for inference over general graphs with convex free energies"
  Abstract: Inference problems in graphical models can be represented as a constrained optimization of a free energy function. It is known that when the Bethe free energy is used, the fixedpoints of the belief propagation (BP) algorithm correspond to the local minima of the free energy. However BP fails to converge in many cases of interest. Moreover, the Bethe free energy is non-convex for graphical models with cycles thus introducing great difficulty in deriving efficient algorithms for finding local minima of the free energy for general graphs. In this paper we introduce two efficient BP-like algorithms, one sequential and the other parallel, that are guaranteed to converge to the global minimum, for any graph, over the class of energies known as "convex free energies". In addition, we propose an efficient heuristic for setting the parameters of the convex free energy based on the structure of the graph.

Connected through hyperedge HE-1 (induced by source paper "convergence rates of smooth message passing with rounding in entropy regularized map inference"):
  with members:
    - "near linear time approximation algorithms for optimal transport via sinkhorn iteration"
    - "map estimation linear programming and belief propagation with convex free energies"
    - "convexifying the bethe free energy"

Connected through hyperedge HE-2 (induced by source paper "graph structured prediction energy networks"):
  with members:
    - "deep structured prediction with nonlinear output transformations"
    - "learning approximate inference networks for structured prediction"
    - "deep value networks learn to evaluate and iteratively refine structured outputs"
    - "end to end sequence labeling via bi directional lstm cnns crf"
    - "neural architectures for named entity recognition"
    - "structured prediction energy networks"
    - "deeply learning the messages in message passing inference"
    - "conditional random fields as recurrent neural networks"

Connected through hyperedge HE-3 (induced by source paper "message passing and combinatorial optimization"):
  with members:
    - "augmentative message passing for traveling salesman problem and graph partitioning"
    - "perturbed message passing for constraint satisfaction problems"
    - "revisiting algebra and complexity of inference in graphical models"
    - "on map inference by mwss on perfect graphs"
    - "loopy belief propagation for approximate inference an empirical study"
    - "graphical models for game theory"
    - "message passing algorithms reparameterizations and splittings"
    - "the bethe permanent of a nonnegative matrix"

Connected through hyperedge HE-4 (induced by source paper "decomposition bounds for marginal map"):
  with members:
    - "message passing algorithms reparameterizations and splittings"
    - "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"
    - "join graph based cost shifting schemes"
    - "solving map exactly using systematic search"
    - "a new class of upper bounds on the log partition function"
    - "tightening fractional covering upper bounds on the partition function for high order region graphs"

Connected through hyperedge HE-5 (induced by source paper "projecting ising model parameters for fast mixing"):
  with members:
    - "a generalized mean field algorithm for variational inference in exponential families"
    - "a new class of upper bounds on the log partition function"

Connected through hyperedge HE-6 (induced by source paper "projecting markov random field parameters for fast mixing"):
  with members:
    - "projecting ising model parameters for fast mixing"
    - "expectation propagation for approximate bayesian inference"

Connected through hyperedge HE-7 (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"
    - "iterative join graph propagation"
    - "residual belief propagation informed scheduling for asynchronous message passing"
    - "structured region graphs morphing ep into gbp"
    - "convergent message passing algorithms a unifying view"

Connected through hyperedge HE-8 (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"
    - "map estimation linear programming and belief propagation with convex free energies"
    - "convergent propagation algorithms via oriented trees"
    - "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)