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: "getting feasible variable estimates from infeasible ones mrf local polytope study"
  Abstract: This paper proposes a method for construction of approximate feasible primal solutions from dual ones for large-scale optimization problems possessing certain separability properties. Whereas infeasible primal estimates can typically be produced from (sub-)gradients of the dual function, it is often not easy to project them to the primal feasible set, since the projection itself has a complexity comparable to the complexity of the initial problem. We propose an alternative efficient method to obtain feasibility and show that its properties influencing the convergence to the optimum are similar to the properties of the Euclidean projection. We apply our method to the local polytope relaxation of inference problems for Markov Random Fields and demonstrate its superiority over existing methods.

Connected through hyperedge HE-1 (induced by source paper "maximum persistency via iterative relaxed inference with graphical models"):
  with members:
    - "higher order maximum persistency and comparison theorems"
    - "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"
    - "potts model parametric maxflow and k submodular functions"
    - "efficient mrf energy minimization via adaptive diminishing smoothing"
    - "the power of linear programming for valued csps a constructive characterization"
    - "generalized roof duality and bisubmodular functions"

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"
    - "on the optimality of tree reweighted max product message passing"
    - "submodular decomposition framework for inference in associative markov networks with global constraints"

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"
    - "efficiently searching for frustrated cycles in map inference"
    - "submodular decomposition framework for inference in associative markov networks with global constraints"
    - "continuous multiclass labeling approaches and algorithms"

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.NA(Numerical Analysis)