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: "efficient mrf energy minimization via adaptive diminishing smoothing"
  Abstract: We consider the linear programming relaxation of an energy minimization problem for Markov Random Fields. The dual objective of this problem can be treated as a concave and unconstrained, but non-smooth function. The idea of smoothing the objective prior to optimization was recently proposed in a series of papers. Some of them suggested the idea to decrease the amount of smoothing (so called temperature) while getting closer to the optimum. However, no theoretical substantiation was provided. We propose an adaptive smoothing diminishing algorithm based on the duality gap between relaxed primal and dual objectives and demonstrate the efficiency of our approach with a smoothed version of Sequential Tree-Reweighted Message Passing (TRW-S) algorithm. The strategy is applicable to other algorithms as well, avoids adhoc tuning of the smoothing during iterations, and provably guarantees convergence to the optimum.

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"
    - "efficiently searching for frustrated cycles in map inference"
    - "lagrangian relaxation for map estimation in graphical models"

Connected through hyperedge HE-2 (induced by source paper "continuous relaxation of map inference a nonconvex perspective"):
  with members:
    - "alternating direction graph matching"
    - "a new look at reweighted message passing"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "opengm a c library for discrete graphical models"
    - "efficiently searching for frustrated cycles in map inference"

Connected through hyperedge HE-3 (induced by source paper "a new look at reweighted message passing"):
  with members:
    - "generalized sequential tree reweighted message passing"
    - "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"
    - "lagrangian relaxation for map estimation in graphical models"

Connected through hyperedge HE-4 (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"
    - "getting feasible variable estimates from infeasible ones mrf local polytope study"
    - "the power of linear programming for valued csps a constructive characterization"
    - "generalized roof duality and bisubmodular functions"

Connected through hyperedge HE-5 (induced by source paper "diffusion methods for classification with pairwise relationships"):
  with members:
    - "convergent message passing algorithms a unifying view"

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 "a comparative study of modern inference techniques for structured discrete energy minimization problems"):
  with members:
    - "on measure concentration of random maximum a posteriori perturbations"
    - "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"
    - "submodular decomposition framework for inference in associative markov networks with global constraints"
    - "continuous multiclass labeling approaches and algorithms"

Connected through hyperedge HE-8 (induced by source paper "reflection methods for user friendly submodular optimization"):
  with members:
    - "a distributed mincut maxflow algorithm combining path augmentation and push relabel"
    - "learning with submodular functions a convex optimization perspective"
    - "efficient minimization of decomposable submodular functions"
    - "minimizing a sum of submodular functions"

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)