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: "on the optimality of tree reweighted max product message passing"
  Abstract: Tree-reweighted max-product (TRW) message passing is a modified form of the ordinary max-product algorithm for attempting to find minimal energy configurations in Markov random field with cycles. For a TRW fixed point satisfying the strong tree agreement condition, the algorithm outputs a configuration that is provably optimal. In this paper, we focus on the case of binary variables with pairwise couplings, and establish stronger properties of TRW fixed points that satisfy only the milder condition of weak tree agreement (WTA). First, we demonstrate how it is possible to identify part of the optimal solution|i.e., a provably optimal solution for a subset of nodes| without knowing a complete solution. Second, we show that for submodular functions, a WTA fixed point always yields a globally optimal solution. We establish that for binary variables, any WTA fixed point always achieves the global maximum of the linear programming relaxation underlying the TRW method.

Connected through hyperedge HE-1 (induced by source paper "partial optimality of dual decomposition for map inference in pairwise mrfs"):
  with members:
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "a compact linear programming relaxation for binary sub modular mrf"
    - "a tutorial on dual decomposition and lagrangian relaxation for inference in natural language processing"
    - "tightening lp relaxations for map using message passing"

Connected through hyperedge HE-2 (induced by source paper "submodular relaxation for inference in markov random fields"):
  with members:
    - "tightening mrf relaxations with planar subproblems"
    - "getting feasible variable estimates from infeasible ones mrf local polytope study"
    - "submodular decomposition framework for inference in associative markov networks with global constraints"

Connected through hyperedge HE-3 (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-4 (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"
    - "norm product belief propagation primal dual message passing for approximate inference"
    - "equivalence of lp relaxation and max product for weighted matching in general graphs"
    - "lagrangian relaxation for map estimation in graphical models"

Connected through hyperedge HE-5 (induced by source paper "message passing algorithms reparameterizations and splittings"):
  with members:
    - "loopy belief propagation for approximate inference an empirical study"
    - "counting in graph covers a combinatorial characterization of the bethe entropy function"
    - "map estimation linear programming and belief propagation with convex free energies"
    - "convergent message passing algorithms a unifying view"
    - "message passing for maximum weight independent set"
    - "norm product belief propagation primal dual message passing for approximate inference"
    - "towards low complexity linear programming decoding"
    - "convergence of the min sum message passing algorithm for quadratic optimization"

Connected through hyperedge HE-6 (induced by source paper "a graphical transformation for belief propagation maximum weight matchings and odd sized cycles"):
  with members:
    - "convergent message passing algorithms a unifying view"
    - "belief propagation for weighted b matchings on arbitrary graphs and its relation to linear programs with integer solutions"
    - "message passing for maximum weight independent set"
    - "max product for maximum weight matching convergence correctness and lp duality"

Connected through hyperedge HE-7 (induced by source paper "tightening lp relaxations for map using message passing"):
  with members:
    - "on the generation of alternative explanations with implications for belief revision"
    - "on the choice of regions for generalized belief propagation"
    - "residual belief propagation informed scheduling for asynchronous message passing"
    - "map estimation linear programming and belief propagation with convex free energies"
    - "structured region graphs morphing ep into gbp"

Connected through hyperedge HE-8 (induced by source paper "node splitting a scheme for generating upper bounds in bayesian networks"):
  with members:
    - "empirical evaluation of approximation algorithms for probabilistic decoding"
    - "systematic vs non systematic algorithms for solving the mpe task"
    - "iterative join graph propagation"
    - "a variational approach for approximating bayesian networks by edge deletion"
    - "exploiting causal independence in bayesian network 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)