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: "lagrangian relaxation for map estimation in graphical models"
  Abstract: We develop a general framework for MAP es- timation in discrete and Gaussian graphical models using Lagrangian relaxation techniques. The key idea is to refor- mulate an intractable estimation problem as one defined on a more tractable graph, but subject to additional constraints. Relaxing these constraints gives a tractable dual problem, one defined by a thin graph, which is then optimized by an iterative procedure. When this iterative optimization leads to a consistent estimate, one which also satisfies the constraints, then it corresponds to an optimal MAP estimate of the original model. Otherwise there is a "duality gap", and we obtain a bound on the optimal solution. Thus, our approach combines convex optimization with dynamic programming techniques applicable for thin graphs. The popular tree-reweighted max- product (TRMP) method may be seen as solving a particular class of such relaxations, where the intractable graph is relaxed to a set of spanning trees. We also consider relaxations to a set of small induced subgraphs, thin subgraphs (e.g. loops), and a connected tree obtained by "unwinding" cycles. In addition, we propose a new class of multiscale relaxations that introduce "summary" variables. The potential benefits of such generalizations include: reducing or eliminating the "duality gap" in hard problems, reducing the number of Lagrange multipliers in the dual problem, and accelerating convergence of the iterative optimization procedure.

Connected through hyperedge HE-1 (induced by source paper "map inference via l2 sphere linear program reformulation"):
  with members:
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "bethe admm for tree decomposition based parallel map inference"
    - "efficient mrf energy minimization via adaptive diminishing smoothing"
    - "efficiently searching for frustrated cycles in map inference"

Connected through hyperedge HE-2 (induced by source paper "map inference via block coordinate frank wolfe algorithm"):
  with members:
    - "efficient linear programming for dense crfs"
    - "minding the gaps for block frank wolfe optimization of structured svms"
    - "a dual ascent framework for lagrangean decomposition of combinatorial problems"
    - "a new look at reweighted message passing"
    - "submodular relaxation for inference in markov random fields"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "a multi plane block coordinate frank wolfe algorithm for training structural svms with a costly max oracle"
    - "block coordinate frank wolfe optimization for structural svms"

Connected through hyperedge HE-3 (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"
    - "convergent message passing algorithms a unifying view"

Connected through hyperedge HE-4 (induced by source paper "a new look at reweighted message passing"):
  with members:
    - "generalized sequential tree reweighted message passing"
    - "efficient mrf energy minimization via adaptive diminishing smoothing"
    - "the power of linear programming for valued csps"
    - "convergent message passing algorithms a unifying view"
    - "tightening lp relaxations for map using message passing"
    - "efficiently searching for frustrated cycles in map inference"
    - "the power of linear programming for valued csps a constructive characterization"
    - "norm product belief propagation primal dual message passing for approximate inference"

Connected through hyperedge HE-5 (induced by source paper "dual decomposition from the perspective of relax compensate and then recover"):
  with members:
    - "a differential approach to inference in bayesian networks"
    - "approximating the partition function by deleting and then correcting for model edges"
    - "iterative join graph propagation"
    - "tightening lp relaxations for map using message passing"

Connected through hyperedge HE-6 (induced by source paper "efficient semidefinite branch and cut for map mrf inference"):
  with members:
    - "efficient sdp inference for fully connected crfs based on low rank decomposition"
    - "crf learning with cnn features for image segmentation"
    - "maximum persistency via iterative relaxed inference with graphical models"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "maximum persistency in energy minimization"
    - "partial optimality by pruning for map inference with general graphical models"
    - "scalable semidefinite relaxation for maximum a posterior estimation"
    - "a fast semidefinite approach to solving binary quadratic problems"

Connected through hyperedge HE-7 (induced by source paper "tree based iterated local search for markov random fields with applications in image analysis"):
  with members:
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "loopy belief propagation for approximate inference an empirical study"
    - "convergent message passing algorithms a unifying view"
    - "map estimation of semi metric mrfs via hierarchical graph cuts"
    - "on the optimality of tree reweighted max product message passing"
    - "norm product belief propagation primal dual message passing for approximate inference"
    - "equivalence of lp relaxation and max product for weighted matching in general graphs"

Connected through hyperedge HE-8 (induced by source paper "reweighted message passing revisited"):
  with members:
    - "the power of linear programming for valued csps"
    - "convergent message passing algorithms a unifying view"
    - "tightening lp relaxations for map using message passing"
    - "efficiently searching for frustrated cycles in map inference"
    - "norm product belief propagation primal dual message passing for approximate inference"

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)