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: "partial optimality by pruning for map inference with general graphical models"
  Abstract: We consider the energy minimization problem for undirected graphical models, also known as MAP-inference problem for Markov random fields which is NP-hard in general. We propose a novel polynomial time algorithm to obtain a part of its optimal non-relaxed integral solution. Our algorithm is initialized with variables taking integral values in the solution of a convex relaxation of the MAP-inference problem and iteratively prunes those, which do not satisfy our criterion for partial optimality. We show that our pruning strategy is in a certain sense theoretically optimal. Also empirically our method outperforms previous approaches in terms of the number of persistently labelled variables. The method is very general, as it is applicable to models with arbitrary factors of an arbitrary order and can employ any solver for the considered relaxed problem. Our method's runtime is determined by the runtime of the convex relaxation solver for the MAP-inference problem.

Connected through hyperedge HE-1 (induced by source paper "a discriminative view of mrf pre processing algorithms"):
  with members:
    - "full flow optical flow estimation by global optimization over regular grids"
    - "maximum persistency via iterative relaxed inference with graphical models"
    - "maximum persistency in energy minimization"
    - "tightening lp relaxations for map using message passing"
    - "efficiently searching for frustrated cycles in map inference"
    - "continuous multiclass labeling approaches and algorithms"
    - "generalized roof duality and bisubmodular functions"

Connected through hyperedge HE-2 (induced by source paper "image labeling by assignment"):
  with members:
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "optimality bounds for a variational relaxation of the image partitioning problem"
    - "continuous multiclass labeling approaches and algorithms"

Connected through hyperedge HE-3 (induced by source paper "global hypothesis generation for 6d object pose estimation"):
  with members:
    - "going further with point pair features"
    - "maximum persistency via iterative relaxed inference with graphical models"
    - "learning analysis by synthesis for 6d pose estimation in rgb d images"
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "maximum persistency in energy minimization"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

Connected through hyperedge HE-4 (induced by source paper "higher order maximum persistency and comparison theorems"):
  with members:
    - "a new look at reweighted message passing"
    - "maximum persistency via iterative relaxed inference with graphical models"
    - "maximum persistency in energy minimization"
    - "potts model parametric maxflow and k submodular functions"
    - "generalized sequential tree reweighted message passing"
    - "the power of linear programming for valued csps"
    - "efficiently searching for frustrated cycles in map inference"
    - "the power of linear programming for valued csps a constructive characterization"

Connected through hyperedge HE-5 (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"
    - "potts model parametric maxflow and k submodular functions"
    - "efficient mrf energy minimization via adaptive diminishing smoothing"
    - "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-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"
    - "scalable semidefinite relaxation for maximum a posterior estimation"
    - "a fast semidefinite approach to solving binary quadratic problems"
    - "higher order segmentation via multicuts"

Connected through hyperedge HE-7 (induced by source paper "maximum persistency in energy minimization"):
  with members:
    - "potts model parametric maxflow and k submodular functions"
    - "generalized roof duality and bisubmodular 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)