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: "submodular decomposition framework for inference in associative markov networks with global constraints"
  Abstract: In the paper we address the problem of finding the most probable state of discrete Markov random field (MRF) with associative pairwise terms. Although of practical importance, this problem is known to be NP-hard in general. We propose a new type of MRF decomposition, submodular decomposition (SMD). Unlike existing decomposition approaches SMD decomposes the initial problem into subproblems corresponding to a specific class label while preserving the graph structure of each subproblem. Such decomposition enables us to take into account several types of global constraints in an efficient manner. We study theoretical properties of the proposed approach and demonstrate its applicability on a number of problems.

Connected through hyperedge HE-1 (induced by source paper "submodular relaxation for inference in markov random fields"):
  with members:
    - "tightening mrf relaxations with planar subproblems"
    - "on the optimality of tree reweighted max product message passing"
    - "getting feasible variable estimates from infeasible ones mrf local polytope study"

Connected through hyperedge HE-2 (induced by source paper "playing with duality an overview of recent primal dual approaches for solving large scale optimization problems"):
  with members:
    - "proximal reinforcement learning a new theory of sequential decision making in primal dual spaces"
    - "reflection methods for user friendly submodular optimization"
    - "tightening mrf relaxations with planar subproblems"
    - "tightening lp relaxations for map using message passing"
    - "optimization with sparsity inducing penalties"
    - "generalized roof duality and bisubmodular functions"
    - "norm product belief propagation primal dual message passing for approximate inference"

Connected through hyperedge HE-3 (induced by source paper "a comparative study of modern inference techniques for structured discrete energy minimization problems"):
  with members:
    - "on measure concentration of random maximum a posteriori perturbations"
    - "efficient mrf energy minimization via adaptive diminishing smoothing"
    - "opengm a c library for discrete graphical models"
    - "getting feasible variable estimates from infeasible ones mrf local polytope study"
    - "efficiently searching for frustrated cycles in map inference"
    - "continuous multiclass labeling approaches and algorithms"

Connected through hyperedge HE-4 (induced by source paper "higher order segmentation via multicuts"):
  with members:
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "fast planar correlation clustering for image segmentation"
    - "tightening lp relaxations for map using message passing"
    - "efficiently searching for frustrated cycles in map inference"
    - "objective criteria for the evaluation of clustering methods"

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.CV(Computer Vision and Pattern Recognition)