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 a unifying view"
  Abstract: Message-passing algorithms have emerged as powerful techniques for approximate inference in graphical models. When these algorithms converge, they can be shown to find local (or sometimes even global) optima of variational formulations to the inference problem. But many of the most popular algorithms are not guaranteed to converge. This has lead to recent interest in convergent message-passing algorithms. In this paper, we present a unified view of convergent message-passing algorithms. We present a simple derivation of an abstract algorithm, tree-consistency bound optimization (TCBO) that is provably convergent in both its sum and max product forms. We then show that many of the existing convergent algorithms are instances of our TCBO algorithm, and obtain novel convergent algorithms "for free" by exchanging maximizations and summations in existing algorithms. In particular, we show that Wainwright's non-convergent sum-product algorithm for tree based variational bounds, is actually convergent with the right update order for the case where trees are monotonic chains.

Connected through hyperedge HE-1 (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-2 (induced by source paper "end to end training of cnn crf via differentiable dual decomposition"):
  with members:
    - "ccnet criss cross attention for semantic segmentation"
    - "end to end learning for graph decomposition"
    - "non local neural networks"
    - "rethinking atrous convolution for semantic image segmentation"
    - "deep learning markov random field for semantic segmentation"
    - "end to end training of hybrid cnn crf models for stereo"
    - "xception deep learning with depthwise separable convolutions"
    - "deep residual learning for image recognition"

Connected through hyperedge HE-3 (induced by source paper "deep structured prediction with nonlinear output transformations"):
  with members:
    - "learning approximate inference networks for structured prediction"
    - "searnn training rnns with global local losses"
    - "input convex neural networks"
    - "pixel recurrent neural networks"
    - "structured prediction energy networks"
    - "deeply learning the messages in message passing inference"
    - "deep residual learning for image recognition"
    - "fully connected deep structured networks"

Connected through hyperedge HE-4 (induced by source paper "best choice edge grafting for efficient structure learning of markov random fields"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"

Connected through hyperedge HE-5 (induced by source paper "a bayesian algorithm for distributed network localization using distance and direction data"):
  with members:
    - "cooperative simultaneous localization and synchronization in mobile agent networks"
    - "joint device positioning and clock synchronization in 5g ultra dense networks"
    - "localization in mobile networks via virtual convex hulls"
    - "cooperative localization for mobile networks a distributed belief propagation mean field message passing algorithm"
    - "a tractable analysis of the improvement in unique localizability through collaboration"
    - "sub nanosecond time of flight on commercial wi fi cards"
    - "loopy belief propagation for approximate inference an empirical study"
    - "cooperative and distributed localization for wireless sensor networks in multipath environments"

Connected through hyperedge HE-6 (induced by source paper "newton type methods for inference in higher order markov random fields"):
  with members:
    - "a new look at reweighted message passing"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "tightening lp relaxations for map using message passing"
    - "an embarrassingly simple speed up of belief propagation with robust potentials"
    - "norm product belief propagation primal dual message passing for approximate inference"

Connected through hyperedge HE-7 (induced by source paper "online edge grafting for efficient mrf structure learning"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"

Connected through hyperedge HE-8 (induced by source paper "a dual ascent framework for lagrangean decomposition of combinatorial problems"):
  with members:
    - "a message passing algorithm for the minimum cost multicut problem"
    - "a new look at reweighted message passing"
    - "efficient decomposition of image and mesh graphs by lifted multicuts"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "fast planar correlation clustering for image segmentation"
    - "lagrangian relaxation for map estimation in graphical models"

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.AI(Artificial Intelligence)