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: "generalized sequential tree reweighted message passing"
  Abstract: This paper addresses the problem of approximate MAP-MRF inference in general graphical models. Following [36], we consider a family of linear programming relaxations of the problem where each relaxation is specified by a set of nested pairs of factors for which the marginalization constraint needs to be enforced. We develop a generalization of the TRW-S algorithm [9] for this problem, where we use a decomposition into junction chains, monotonic w.r.t. some ordering on the nodes. This generalizes the monotonic chains in [9] in a natural way. We also show how to deal with nested factors in an efficient way. Experiments show an improvement over min-sum diffusion, MPLP and subgradient ascent algorithms on a number of computer vision and natural language processing problems.

Connected through hyperedge HE-1 (induced by source paper "efficient graph cut optimization for full crfs with quantized edges"):
  with members:
    - "memory efficient max flow for multi label submodular mrfs"
    - "efficient continuous relaxations for dense crf"
    - "learning sparse high dimensional filters image filtering dense crfs and bilateral neural networks"
    - "conditional random fields as recurrent neural networks"
    - "the fast bilateral solver"
    - "fully connected deep structured networks"
    - "learning deep structured models"
    - "semantic image segmentation with deep convolutional nets and fully connected crfs"

Connected through hyperedge HE-2 (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"
    - "partial optimality by pruning for map inference with general graphical models"
    - "potts model parametric maxflow and k submodular functions"
    - "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-3 (induced by source paper "a new look at reweighted message passing"):
  with members:
    - "efficient mrf energy minimization via adaptive diminishing smoothing"
    - "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 "submodularization for quadratic pseudo boolean optimization"):
  with members:
    - "efficient regularization of squared curvature"

Connected through hyperedge HE-5 (induced by source paper "convex relaxations for learning bounded treewidth decomposable graphs"):
  with members:
    - "maximum likelihood bounded tree width markov networks"
    - "efficient stepwise selection in decomposable models"
    - "ordering based search a simple and effective algorithm for learning bayesian networks"
    - "pac learning bounded tree width graphical models"
    - "near optimal nonmyopic value of information in graphical models"

Connected through hyperedge HE-6 (induced by source paper "the power of linear programming for valued csps a constructive characterization"):
  with members:
    - "the complexity of conservative valued csps"
    - "the power of linear programming for valued csps"

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.CV(Computer Vision and Pattern Recognition)