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: "maximum persistency in energy minimization"
  Abstract: We consider discrete pairwise energy minimization problem (weighted constraint satisfaction, max-sum labeling) and methods that identify a globally optimal partial assignment of variables. When finding a complete optimal assignment is intractable, determining optimal values for a part of variables is an interesting possibility. Existing methods are based on different sufficient conditions. We propose a new sufficient condition for partial optimality which is: (1) verifiable in polynomial time (2) invariant to reparametrization of the problem and permutation of labels and (3) includes many existing sufficient conditions as special cases. We pose the problem of finding the maximum optimal partial assignment identifiable by the new sufficient condition. A polynomial method is proposed which is guaranteed to assign same or larger part of variables than several existing approaches. The core of the method is a specially constructed linear program that identifies persistent assignments in an arbitrary multi-label setting.

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"
    - "partial optimality by pruning for map inference with general graphical models"
    - "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 "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"
    - "partial optimality by pruning for map inference with general graphical models"
    - "efficient inference in fully connected crfs with gaussian edge potentials"

Connected through hyperedge HE-3 (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"
    - "partial optimality by pruning for map inference with general graphical models"
    - "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-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"
    - "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"
    - "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 "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"
    - "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"
    - "higher order segmentation via multicuts"

Connected through hyperedge HE-6 (induced by source paper "partial optimality by pruning for map inference with general graphical models"):
  with members:
    - "tightening lp relaxations for map using message passing"
    - "efficiently searching for frustrated cycles in map inference"
    - "norm product belief propagation primal dual message passing for approximate inference"

Connected through hyperedge HE-7 (induced by source paper "speeding up graphical model optimization via a coarse to fine cascade of pruning classifiers"):
  with members:
    - "map estimation of semi metric mrfs via hierarchical graph cuts"

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.DM(Discrete Mathematics)