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: "map estimation of semi metric mrfs via hierarchical graph cuts"
  Abstract: We consider the task of obtaining the maximum a posteriori estimate of discrete pairwise random fields with arbitrary unary potentials and semimetric pairwise potentials. For this problem, we propose an accurate hierarchical move making strategy where each move is computed efficiently by solving an st-MINCUT problem. Unlike previous move making approaches, e.g. the widely used a-expansion algorithm, our method obtains the guarantees of the standard linear programming (LP) relaxation for the important special case of metric labeling. Unlike the existing LP relaxation solvers, e.g. interior-point algorithms or tree-reweighted message passing, our method is significantly faster as it uses only the efficient st-MINCUT algorithm in its design. Using both synthetic and real data experiments, we show that our technique outperforms several commonly used algorithms.

Connected through hyperedge HE-1 (induced by source paper "accelerated inference in markov random fields via smooth riemannian optimization"):
  with members:
    - "bonnet an open source training and deployment framework for semantic segmentation in robotics using cnns"
    - "large scale binary quadratic optimization using semidefinite relaxation and applications"
    - "a review on deep learning techniques applied to semantic segmentation"
    - "the cityscapes dataset for semantic urban scene understanding"
    - "deeplab semantic image segmentation with deep convolutional nets atrous convolution and fully connected crfs"
    - "a survey of semantic segmentation"
    - "beyond pixels a comprehensive survey from bottom up to semantic image segmentation and cosegmentation"
    - "efficient sdp inference for fully connected crfs based on low rank decomposition"

Connected through hyperedge HE-2 (induced by source paper "efficient optimization for hierarchically structured interacting segments hints"):
  with members:
    - "volumetric bias in segmentation and reconstruction secrets and solutions"
    - "potts model parametric maxflow and k submodular functions"

Connected through hyperedge HE-3 (induced by source paper "efficient linear programming for dense crfs"):
  with members:
    - "efficient continuous relaxations for dense crf"
    - "minding the gaps for block frank wolfe optimization of structured svms"
    - "efficient sdp inference for fully connected crfs based on low rank decomposition"
    - "conditional random fields as recurrent neural networks"
    - "fully connected deep structured networks"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "a multi plane block coordinate frank wolfe algorithm for training structural svms with a costly max oracle"
    - "duality between subgradient and conditional gradient methods"

Connected through hyperedge HE-4 (induced by source paper "efficient continuous relaxations for dense crf"):
  with members:
    - "principled parallel mean field inference for discrete random fields"
    - "efficient sdp inference for fully connected crfs based on low rank decomposition"
    - "conditional random fields as recurrent neural networks"
    - "fully connected deep structured networks"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"
    - "fully convolutional networks for semantic segmentation"
    - "efficient inference in fully connected crfs with gaussian edge potentials"
    - "block coordinate frank wolfe optimization for structural svms"

Connected through hyperedge HE-5 (induced by source paper "multi way particle swarm fusion"):
  with members:
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "loopy belief propagation for approximate inference an empirical study"

Connected through hyperedge HE-6 (induced by source paper "tree based iterated local search for markov random fields with applications in image analysis"):
  with members:
    - "a comparative study of modern inference techniques for structured discrete energy minimization problems"
    - "loopy belief propagation for approximate inference an empirical study"
    - "convergent message passing algorithms a unifying view"
    - "on the optimality of tree reweighted max product message passing"
    - "norm product belief propagation primal dual message passing for approximate inference"
    - "equivalence of lp relaxation and max product for weighted matching in general graphs"
    - "lagrangian relaxation for map estimation in graphical models"

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:
    - "maximum persistency in energy minimization"

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)